This report is step one of a three step process for data organization in the RESPOND study. The three steps include:
- Data processing: investigation and reporting
- Evaluate the data received from the SEER registries and scantron surveys
- Understand the the variables, their values, distributions, and identify potential problems
- From this report we can propose potential solutions on how to handle missing or unknown data, alternative values, or duplicated or problematic records
- Data cleaning
- Implement solutions for data cleaning given decisions from step 1 report
- Keep data in detailed format
- Create composite variables
- Data creation
- Output data to an “analysis ready” R data set
- Output data to a file that facilitates input into SAS
The working dataset used in this report results in a database join of all Scantron Surveys currently processed with all Cancer Registry files received to date from study centers, which were able to be linked based on available identifiers.
- This working dataset was created on 2.8.2020
- The dataset includes 2167 records
- The dataset includes 532 variables
- Variables include: registryid, naaccrrecordversion, tumorrecordnumber, addratdxstate, countyatdx, countyatdxgeocode2000, countyatdxgeocode2010, censustract2000, censustract2010, maritalstatusatdx, race1, race2, race3, race4, race5, spanishhispanicorigin, nhiaderivedhisporigin, ihslink, racenapiia, sex, ageatdiagnosis, dateofbirth, birthplace, birthplacestate, birthplacecountry, censusblockgroup2000, censusblockgroup2010, censustrcertainty2000, censustrcertainty2010, sequencenumbercentral, dateofdiagnosis, primarysite, grade, diagnosticconfirmation, typeofreportingsource, histologictypeicdo3, behaviorcodeicdo3, primarypayeratdx, seersummarystage2000, seersummarystage1977, derivedsummarystage2018, summarystage2018, eodprimarytumor, eodregionalnodes, eodmets, derivedeod2018t, eodextension, derivedeod2018m, eodextensionprostpath, eodlymphnodeinvolv, derivedeod2018n, derivedeod2018stagegroup, regionalnodespositive, regionalnodesexamined, tnmpatht, tnmpathn, tnmpathm, tnmpathstagegroup, tnmclint, tnmclinn, tnmclinm, tnmclinstagegroup, ajcctnmclint, ajcctnmclinn, ajcctnmclinm, ajcctnmclinstagegroup, ajcctnmpatht, ajcctnmpathn, ajcctnmpathm, ajcctnmpathstagegroup, tumormarker2, rxdatesurgery, rxdateradiation, rxdatechemo, rxdatehormone, rxdatebrm, rxdateother, dateinitialrxseer, rxdatedxstgproc, rxsummtreatmentstatus, rxsummsurgprimsite, rxsummscopereglnsur, rxsummsurgothregdis, reasonfornosurgery, rxsummradiation, rxsummsurgradseq, rxsummchemo, rxsummhormone, rxsummbrm, rxsummother, radregionaldosecgy, radregionalrxmodality, rxsummsystemicsurseq, rxsummsurgsite9802, rxsummscopereg9802, rxsummsurgoth9802, dateoflastcontact, vitalstatus, survdateactivefollowup, survdatepresumedalive, survdatedxrecode, causeofdeath, csextension, cslymphnodes, csmetsatdx, cssitespecificfactor7, cssitespecificfactor8, cssitespecificfactor9, cssitespecificfactor10, cssitespecificfactor11, cssitespecificfactor12, cssitespecificfactor13, cssitespecificfactor14, cssitespecificfactor15, cssitespecificfactor1, cssitespecificfactor2, cssitespecificfactor3, cssitespecificfactor4, cssitespecificfactor5, cssitespecificfactor6, derivedajcc6t, derivedajcc6n, derivedajcc6m, derivedajcc6stagegrp, derivedss1977, derivedss2000, comorbidcomplication1, comorbidcomplication2, comorbidcomplication3, comorbidcomplication4, comorbidcomplication5, comorbidcomplication6, comorbidcomplication7, comorbidcomplication8, comorbidcomplication9, comorbidcomplication10, icdrevisioncomorbid, rxdatemostdefinsurg, radboostrxmodality, radboostdosecgy, rxdatesystemic, rxsummtransplntendocr, derivedajcc7t, derivedajcc7n, derivedajcc7m, derivedajcc7stagegrp, derivedseerpathstggrp, derivedseerclinstggrp, derivedseercmbstggrp, derivedseercombinedt, derivedseercombinedn, derivedseercombinedm, secondarydiagnosis1, secondarydiagnosis2, secondarydiagnosis3, secondarydiagnosis4, secondarydiagnosis5, secondarydiagnosis6, secondarydiagnosis7, secondarydiagnosis8, secondarydiagnosis9, secondarydiagnosis10, gleasonpatternsclinical, gleasonpatternspathological, gleasonscoreclinical, gleasonscorepathological, gleasontertiarypattern, gradeclinical, gradepathological, numberofcoresexamined, numberofcorespositive, prostatepathologicalextension, psalabvalue, rid, recno, siteid, surveyid, locationname, respondid, methodology, a1month, a1year, a1not, a2, a3_1, a3_2, a3_3, a3_4, a3_5, a3_6, a3_7, a3_8, a3_9, a3_10, a3_11, a3_12, a3_13, a3_14, a3_15, a3_16, a3_17, a3_18, a3_19, a3_20, a3_21, a3_22, a3_23, a3_24, a3other, a4month, a4year, a5, a5other, a6, a6other, a7, a7other, a8, b1aa, b1ab, b1ac, b1bno, b1ba, b1ba2, b1bb, b1bc, b1cno, b1ca, b1ca2, b1cb, b1cc, b1da, b1db, b1dc, b1ea, b1eb, b1ec, b2, b2a_1, b2a_2, b2a_3, b2a_4, b2a_5, b2a_6, b2a_7, b2b_1, b2b_3, b2b_4, b2b_5, b2b_6, b2b_7, b2cno, b2c_1, b2c_2, b2c_3, b2c_4, b2c_5, b2c_6, b2c_7, b2dno, b2d_1, b2d_3, b2d_4, b2d_5, b2d_6, b2d_7, b2eno, b2e_1, b2e_2, b2e_3, b2e_4, b2e_5, b2e_6, b2e_7, b2fno, b2f_1, b2f_3, b2f_4, b2f_5, b2f_6, b2f_7, b3, b4aa, b4ab, b4ba, b4bb, b4ca, b4cb, b4da, b4db, b4dc, b4ea, b4eb, b4fa, b4fb, b4fc, b4ga, b4gb, b4ha, b4hb, b4ia, b4ib, b4ja, b4jb, b4jc, b4jd, b4ka, b4kb, b4la, b4lb, b4ma, b4mb, b4na, b4nb, b4oa, b4ob, b4pa, b4pb, b4qa, b4qb, b4qother, b5, b5other, c1, c2a1, c2a2, c2a3, c2b1, c2b2, c2b3, c2c1, c2c2, c2c3, c3a1, c3a2, c3a3, c3b1, c3b2, c3b3, c3c1, c3c2, c3c3, c3d1, c3d2, c3d3, c4a1, c4a2, c4a3, c4b1, c4b2, c4b3, c4c1, c4c2, c4c3, c4d1, c4d2, c4d3, c4e1, c4e2, c4e3, d1aa, d1ab, d1ba, d1bb, d1ca, d1cb, d1da, d1db, d1ea, d1eb, d1fa, d1fb, d1ga, d1gb, d2a, d2b, d2c, d2d, d2e, d3a1, d3a2, d3a3, d3b1, d3b2, d3b3, d3c1, d3c2, d3c3, d3d1, d3d2, d3d3, d3e1, d3e2, d3e3, d3f1, d3f2, d3f3, d3g1, d3g2, d3g3, d3h1, d3h2, d3h3, d3i1, d3i2, d3i3, d3j1, d3j2, d3j3, d4a, d4b, d4c, d4d, d4e, d4f, d4g, d4h, d4i, d4j, d4k, d4l, d5a, d5b, d5c, d5d, d5e, d5f, d5g, d5h, d5i, d5j, d5k, e1_1, e1_2, e1_3, e1_4, e1_5, e1_6, e1other, e2aa, e2ab, e2ba, e2bb, e3, e4, e5, e6, e7, e8, e9_1, e9_2, e9_3, e9_4, e9_5, e9_6, e9_7, e9_8, e9_9, e9_10, e10_1, e10_2, e10_3, e10_4, e10_5, e10_6, e10_7, e10_8, e10_3_1, e10_3_2, e10_3_3, e10_4_1, e10_4_2, e10_4_3, e10_5_1, e10_5_2, e10_5_3, e10_5_4, e10_5_5, e11a, e11b, e11c, e11d, e11e, e11f, e12, e13, e14, e15, f1ft, f1in, f1cm, f2lbs, f2kgs, f3, f4, f5, f6, f7, f7age, f7a, f7b, f7bage, g1, g2_1, g2_2, g2_3, g2_4, g2_5, g3, g3other, g4a, g4b, g4c, g5, g5other, g6_1, g6_2, g6_3, g6_4, g6_5, g6_6, g6_7, g6_8, g7, g8, g9a, g9b, g9c, g10, g10other, g11, g12
SITE ID
- Codes
- 10 Greater CA
- 20 Georgia
- 25 North Carolina
- 30 Northern CA
- 40 Louisiana
- 50 New Jersey
- 60 Detroit
- 61 Michigan
- 70 Texas
- 80 Los Angeles County
- 81 USC-Other
- 82 USC-MEC
- 90 New York
- 94 Florida
- 95 WebRecruit-Limbo
- 99 WebRecruit
siteid <- as.factor(trimws(d[,"siteid"]))
#new.d.n <- data.frame(new.d.n, siteid) # keep NAACCR coding
# NEED REGISTRY NAMES!!
#replace number with names
levels(siteid)[levels(siteid)=="80"] <- "Los Angeles County.80"
levels(siteid)[levels(siteid)=="30"] <- "Northern CA.30"
levels(siteid)[levels(siteid)=="10"] <- "Greater CA.10"
levels(siteid)[levels(siteid)=="60"] <- "Detroit.60"
levels(siteid)[levels(siteid)=="40"] <- "Louisiana.40"
levels(siteid)[levels(siteid)=="20"] <- "Georgia.20"
levels(siteid)[levels(siteid)=="61"] <- "Michigan.61"
new.d <- data.frame(new.d, siteid)
new.d <- apply_labels(new.d, siteid = "Site ID")
new.d.1 <- data.frame(new.d.1, siteid)
#cro(new.d$siteid) # this is pretty but doesn't show NAs
#summary(new.d$siteid)
#Using kable function to form a nice table
siteid_count<-count(new.d$siteid)
colnames(siteid_count)<- c("Registry", "Total")
kable(siteid_count, format = "simple", align = 'l', caption = "Overview of 7 Registries")
Overview of 7 Registries
| Greater CA.10 |
237 |
| Georgia.20 |
1087 |
| Northern CA.30 |
174 |
| Louisiana.40 |
290 |
| Detroit.60 |
174 |
| Michigan.61 |
16 |
| Los Angeles County.80 |
189 |
NAACCR RECORD VERSION
- Description: This item applies only to record types I, C, A, and M. Code the NAACCR record version used to create the record. The correction record (U) has its own record version data item.
- Rationale: The NAACCR Layout version is necessary to communicate to the recipient of data in NAACCR form where the various items are found and how they are coded. It should be added to the record when the recorded is created.
- Codes
- 120 2010 Version 12
- 121 2011 Version 12.1
- 122 2012 Version 12.2
- 130 2013 Version 13
- 140 2014 Version 14
- 150 2015 Version 15
- 160 2016 Version 16
- 180 2018 Version 18
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#50
naaccrrecordversion <- as.factor(trimws(d[,"naaccrrecordversion"]))
levels(naaccrrecordversion)[levels(naaccrrecordversion)=="180"] <- "2018_Version_18.180"
new.d <- data.frame(new.d, naaccrrecordversion)
new.d <- apply_labels(new.d, naaccrrecordversion = "naaccr Record Version")
new.d.1 <- data.frame(new.d.1, naaccrrecordversion)
#cro(new.d$siteid) # this is pretty but doesn't show NAs
#summary(new.d$siteid)
#Using kable function to form a nice table
naaccrrecordversion<-count(new.d$naaccrrecordversion)
colnames(naaccrrecordversion)<- c("Version", "Total")
kable(naaccrrecordversion, format = "simple", align = 'l', caption = "Overview of Version")
Overview of Version
| 2018_Version_18.180 |
2167 |
TUMOR RECORD NUMBER
- Description: A system-generated number assigned to each tumor. The number should never change even if the tumor sequence is changed or a record (tumor) is deleted.
- Rationale: This is a unique number that identifies a specific tumor so data can be linked. “Sequence Number” cannot be used as a link because the number is changed if a report identifies an earlier tumor or if a tumor record is deleted.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#60
All data
st_css() #IMPORTANT!
tumorrecordnumber <- as.factor(trimws(d[,"tumorrecordnumber"]))
new.d <- data.frame(new.d, tumorrecordnumber)
new.d <- apply_labels(new.d, tumorrecordnumber = "tumor record number")
new.d.1 <- data.frame(new.d.1, tumorrecordnumber)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, tumorrecordnumber)
summarytools::view(dfSummary(new.d$tumorrecordnumber, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumorrecordnumber
[labelled, factor] |
tumor record number |
1. 0
2. 1
3. 2
4. 3
5. 4 |
| 402 | ( | 18.6% | ) | | 1648 | ( | 76.0% | ) | | 102 | ( | 4.7% | ) | | 12 | ( | 0.6% | ) | | 3 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumor_record_number
[factor] |
1. 0
2. 1
3. 2
4. 3
5. 4 |
| 178 | ( | 94.2% | ) | | 4 | ( | 2.1% | ) | | 5 | ( | 2.6% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumor_record_number
[factor] |
1. 0
2. 1
3. 2
4. 3
5. 4 |
| 0 | ( | 0.0% | ) | | 165 | ( | 94.8% | ) | | 6 | ( | 3.4% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumor_record_number
[factor] |
1. 0
2. 1
3. 2
4. 3
5. 4 |
| 224 | ( | 94.5% | ) | | 2 | ( | 0.8% | ) | | 11 | ( | 4.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumor_record_number
[factor] |
1. 0
2. 1
3. 2
4. 3
5. 4 |
| 0 | ( | 0.0% | ) | | 155 | ( | 89.1% | ) | | 15 | ( | 8.6% | ) | | 3 | ( | 1.7% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumor_record_number
[factor] |
1. 0
2. 1
3. 2
4. 3
5. 4 |
| 0 | ( | 0.0% | ) | | 278 | ( | 95.9% | ) | | 10 | ( | 3.4% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumor_record_number
[factor] |
1. 0
2. 1
3. 2
4. 3
5. 4 |
| 0 | ( | 0.0% | ) | | 1029 | ( | 94.7% | ) | | 54 | ( | 5.0% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumor_record_number
[factor] |
1. 0
2. 1
3. 2
4. 3
5. 4 |
| 0 | ( | 0.0% | ) | | 15 | ( | 93.8% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
ADDR AT DX–STATE
Description: Identifies the patient’s state or province of residence at the time of diagnosis as identified by the Reporting Source. For consolidated records, the state may be based on reported or corrected residential address information.
Rationale: The state of residence is part of the patient’s demographic data and has multiple uses. It can be used to evaluate referral patterns, allows for the analysis of cancer cluster concerns, and supports epidemiological studies that use area-based social measures.
Instructions for Coding
- This field is intended to store residential state for the patient’s physical, residential address. The state for PO Box mailing address should not be entered into this data item except in the infrequent case when no other address information is available.
- If the patient has multiple tumors, state at diagnosis may be different for each tumor.
- Do not update this item if the patient’s residential address changes. Store address update information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a state during the geocoding or consolidation process.
- Use the U.S. Postal Service abbreviation (for the state, territory, commonwealth, U.S. possession) or Canada Post abbreviation (for the Canadian province/territory) in which the patient resides at the time the reportable tumor is diagnosed.
- If the patient is a foreign resident, then code either XX or YY depending on the circumstance.
Codes (in addition to USPS abbreviations)
- CD Resident of Canada, NOS (province/territory unknown)
- US Resident of United States, NOS (state/commonwealth/territory/possession unknown)
- XX Resident of country other than the United States (including its territories, commonwealths, or possessions) or Canada, and country is known
- YY Resident of country other than the United States (including its territories, commonwealths, or possessions) or Canada, and country is unknown
- ZZ Residence unknow
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#80
All data
st_css() #IMPORTANT!
addratdxstate <- as.factor(trimws(d[,"addratdxstate"]))
new.d <- data.frame(new.d, addratdxstate)
new.d <- apply_labels(new.d, addratdxstate = "addr at dx--state")
new.d.1 <- data.frame(new.d.1, addratdxstate)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, addratdxstate)
summarytools::view(dfSummary(new.d$addratdxstate, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
addratdxstate
[labelled, factor] |
addr at dx--state |
1. CA
2. GA
3. LA
4. MI |
| 600 | ( | 27.7% | ) | | 1087 | ( | 50.2% | ) | | 290 | ( | 13.4% | ) | | 190 | ( | 8.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
addr_at_dx_state
[factor] |
1. CA
2. GA
3. LA
4. MI |
| 189 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
addr_at_dx_state
[factor] |
1. CA
2. GA
3. LA
4. MI |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
addr_at_dx_state
[factor] |
1. CA
2. GA
3. LA
4. MI |
| 237 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
addr_at_dx_state
[factor] |
1. CA
2. GA
3. LA
4. MI |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 174 | ( | 100.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
addr_at_dx_state
[factor] |
1. CA
2. GA
3. LA
4. MI |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 290 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
addr_at_dx_state
[factor] |
1. CA
2. GA
3. LA
4. MI |
| 0 | ( | 0.0% | ) | | 1087 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
addr_at_dx_state
[factor] |
1. CA
2. GA
3. LA
4. MI |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 16 | ( | 100.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COUNTY AT DX REPORTED
Description: Identifies the
Description: Code for the county of the patients residence at the time of diagnosis as identified by the Reporting Source. For U.S. residents, standard codes are those of the FIPS publication Counties and Equivalent Entities of the United States, Its Possessions, and Associated Areas or their equivalent INCITS codes.
Calculating county and county-based variable rates using this item is not recommended. The more specific, geocoded county items should be used when available.
Rationale: This data item may be used for epidemiological purposes. For example, to measure cancer incidence in a particular geographic area.
Instructions for Coding
- This field is intended to store address information for the patient’s physical, residential address. All efforts should be made to find the patient’s true street address and postal code, including reviewing relevant sources outside the medical record if available. The county for a PO Box mailing address should only be recorded when no other address information is available in the medical record and no other information sources are available.
- If the patient has multiple tumors, county at diagnosis may be different for each tumor.
- Do not update this item if the patient’s county of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct county during a consolidation process.
- This variable is coded at time of abstracting and is considered less accurate than the derived, geocoded county at diagnosis variables: County at Diagnosis 1990, 2000, 2010, & 2020.
- Detailed standards have not been set for Canadian provinces/territories. Use code 998 for Canadian residents.
Codes (in addition to FIPS and Geocodes)
- 001-997 Valid FIPS code
- 998 Known town, city, state, or country of residence but county code not known AND a resident outside of the state of reporting institution (must meet all criteria). Use this code for Canadian residents.
- 999 The county of the patient is unknown, or the patient is not a United States resident. County is not documented in the patient’s medical record.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#90
All data
st_css() #IMPORTANT!
countyatdx <- as.factor(trimws(d[,"countyatdx"]))
new.d <- data.frame(new.d, countyatdx)
new.d <- apply_labels(new.d, countyatdx = "addr at dx--state")
new.d.1 <- data.frame(new.d.1, countyatdx)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, countyatdx)
summarytools::view(dfSummary(new.d$countyatdx, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
countyatdx
[labelled, factor] |
addr at dx--state |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 75 | ( | 3.5% | ) | | 4 | ( | 0.2% | ) | | 18 | ( | 0.8% | ) | | 10 | ( | 0.5% | ) | | 5 | ( | 0.2% | ) | | 7 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 11 | ( | 0.5% | ) | | 25 | ( | 1.2% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 5 | ( | 0.2% | ) | | 143 | ( | 6.6% | ) | | 1 | ( | 0.0% | ) | | 40 | ( | 1.8% | ) | | 11 | ( | 0.5% | ) | | 2 | ( | 0.1% | ) | | 47 | ( | 2.2% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 63 | ( | 2.9% | ) | | 1 | ( | 0.0% | ) | | 8 | ( | 0.4% | ) | | 5 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 9 | ( | 0.4% | ) | | 32 | ( | 1.5% | ) | | 9 | ( | 0.4% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 136 | ( | 6.3% | ) | | 1 | ( | 0.0% | ) | | 4 | ( | 0.2% | ) | | 18 | ( | 0.8% | ) | | 5 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 14 | ( | 0.6% | ) | | 4 | ( | 0.2% | ) | | 16 | ( | 0.7% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 6 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 26 | ( | 1.2% | ) | | 1 | ( | 0.0% | ) | | 35 | ( | 1.6% | ) | | 17 | ( | 0.8% | ) | | 1 | ( | 0.0% | ) | | 12 | ( | 0.6% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 45 | ( | 2.1% | ) | | 20 | ( | 0.9% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 9 | ( | 0.4% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 10 | ( | 0.5% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 6 | ( | 0.3% | ) | | 8 | ( | 0.4% | ) | | 1 | ( | 0.0% | ) | | 13 | ( | 0.6% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 7 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 43 | ( | 2.0% | ) | | 2 | ( | 0.1% | ) | | 193 | ( | 8.9% | ) | | 6 | ( | 0.3% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 10 | ( | 0.5% | ) | | 841 | ( | 38.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 189 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 73 | ( | 42.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 39 | ( | 22.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 60 | ( | 34.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 4.6% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 205 | ( | 86.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 34 | ( | 19.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 125 | ( | 71.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 15 | ( | 8.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 1 | ( | 0.3% | ) | | 4 | ( | 1.4% | ) | | 13 | ( | 4.5% | ) | | 9 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 5 | ( | 1.7% | ) | | 4 | ( | 1.4% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 3 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 15 | ( | 5.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 39 | ( | 13.4% | ) | | 1 | ( | 0.3% | ) | | 4 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 148 | ( | 51.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 2.1% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 139 | ( | 12.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 0.7% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 63 | ( | 5.8% | ) | | 1 | ( | 0.1% | ) | | 8 | ( | 0.7% | ) | | 5 | ( | 0.5% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 32 | ( | 2.9% | ) | | 9 | ( | 0.8% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 3 | ( | 0.3% | ) | | 5 | ( | 0.5% | ) | | 4 | ( | 0.4% | ) | | 3 | ( | 0.3% | ) | | 7 | ( | 0.6% | ) | | 1 | ( | 0.1% | ) | | 14 | ( | 1.3% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 6 | ( | 0.6% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 26 | ( | 2.4% | ) | | 1 | ( | 0.1% | ) | | 35 | ( | 3.2% | ) | | 17 | ( | 1.6% | ) | | 1 | ( | 0.1% | ) | | 12 | ( | 1.1% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 45 | ( | 4.1% | ) | | 20 | ( | 1.8% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 9 | ( | 0.8% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 10 | ( | 0.9% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 7 | ( | 0.6% | ) | | 6 | ( | 0.6% | ) | | 8 | ( | 0.7% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 8 | ( | 0.7% | ) | | 410 | ( | 37.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COUNTY AT DX GEOCODE2000
Description: Code for the county of the patient’s residence at the time the tumor was diagnosed is a derived (geocoded) variable based on Census Boundary files from 2000 Decennial Census. This code should be used for county and county-based (such as CHSDA) rates and analysis for all cases diagnosed in 2000-2009.
Rationale: Census tracts are areas geographically nested within counties and designated with a 6-digit number code. This 6-digit code is commonly repeated within a state in different counties. Census tract numbers are only unique when paired with the state and the county. Therefore, a tract cannot be accurately identified without knowing the county. Example from Massachusetts: Rural Franklin County contains a tract 040600 with 2010 population 4,612 people. Urban Suffolk County contains a tract 040600 with 2,444 people. The county must be known in order to distinguish between the two tract codes. Because we historically used a single variable for county at diagnosis [90], correct tract codes were frequently paired with the wrong county due to incorrect county assignment during abstracting or a change of county over time. Also, some variables, such as the Census Tr Poverty Indicatr [145] require the use of the decennial Census County codes closest to year of diagnosis and not the decade of year of diagnosis. Using a single county at diagnosis, and using the reported versus geocoded data, may result in erroneous assignment of geographic location as well as invalid links with census data (i.e., population, poverty category, urban/rural designation).
Instructions for Coding
- This variable is generated through the process of geocoding either during abstracting or at the central registry level.
- It is recommended that all cases diagnosed through 2009 have a geocoded County at Diagnosis 2000.
- At a minimum, all cases diagnosed through 1996-2009 should have a geocoded County at Diagnosis 2000. Cases diagnosed 1996-1999 must have both County at Diagnosis 1990 and County at Diagnosis 2000 codes for proper assignment of the Census Tract Poverty Indicator [145]. Cases diagnosed 2006-2009 must have both County at Diagnosis 2000 and County at Diagnosis 2010 codes for proper assignment of the Census Tract Poverty Indicator [145].
- If the patient has multiple tumors, geocoded county may be different for each tumor.
- Do not update this item if the patient’s county of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a county during manual geocoding or a consolidation process.
- PO Box address information should not be used to geocode this data item except in the infrequent case when no other address information is available.
- Detailed standards have not been set for Canadian provinces/territories. Use code 998 for Canadian residents.
- Blank “Not geocoded” is allowable for cases diagnosed before 1995 and after 2009. However, it is recommended to have all cases geocoded to a 2000 Census County to allow for both retrospective and cross-sectional analyses.
Codes
- 001-997 County at diagnosis. Valid FIPS code.
- 998 Known town, city, state, or country of residence but county code not known AND a resident outside of the state of reporting institution (must meet all criteria). Use this code for Canadian residents.
- 999 County unknown. The county of the patient is unknown, or the patient is not a United States resident. County is not documented in the patient’s medical record.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#95
All data
st_css() #IMPORTANT!
countyatdxgeocode2000 <- as.factor(trimws(d[,"countyatdxgeocode2000"]))
new.d <- data.frame(new.d, countyatdxgeocode2000)
new.d <- apply_labels(new.d, countyatdxgeocode2000 = "county at dx geocode 2000")
new.d.1 <- data.frame(new.d.1, countyatdxgeocode2000)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, countyatdxgeocode2000)
summarytools::view(dfSummary(new.d$countyatdxgeocode2000, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
countyatdxgeocode2000
[labelled, factor] |
county at dx geocode 2000 |
1. 103
2. 107
3. 111
4. 113
5. 125
6. 163
7. 19
8. 25
9. 29
10. 59
11. 61
12. 65
13. 67
14. 71
15. 73
16. 77
17. 83
18. 95
19. 97
20. 99 |
| 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 11 | ( | 4.5% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.8% | ) | | 5 | ( | 2.0% | ) | | 7 | ( | 2.9% | ) | | 1 | ( | 0.4% | ) | | 10 | ( | 4.1% | ) | | 17 | ( | 6.9% | ) | | 2 | ( | 0.8% | ) | | 31 | ( | 12.7% | ) | | 31 | ( | 12.7% | ) | | 51 | ( | 20.8% | ) | | 35 | ( | 14.3% | ) | | 11 | ( | 4.5% | ) | | 2 | ( | 0.8% | ) | | 16 | ( | 6.5% | ) | | 4 | ( | 1.6% | ) | | 6 | ( | 2.4% | ) |
|
 |
1922
(88.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx_geocode_2000
[factor] |
1. 103
2. 107
3. 111
4. 113
5. 125
6. 163
7. 19
8. 25
9. 29
10. 59
11. 61
12. 65
13. 67
14. 71
15. 73
16. 77
17. 83
18. 95
19. 97
20. 99 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx_geocode_2000
[factor] |
1. 103
2. 107
3. 111
4. 113
5. 125
6. 163
7. 19
8. 25
9. 29
10. 59
11. 61
12. 65
13. 67
14. 71
15. 73
16. 77
17. 83
18. 95
19. 97
20. 99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx_geocode_2000
[factor] |
1. 103
2. 107
3. 111
4. 113
5. 125
6. 163
7. 19
8. 25
9. 29
10. 59
11. 61
12. 65
13. 67
14. 71
15. 73
16. 77
17. 83
18. 95
19. 97
20. 99 |
| 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 11 | ( | 4.6% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 3.0% | ) | | 1 | ( | 0.4% | ) | | 10 | ( | 4.2% | ) | | 17 | ( | 7.2% | ) | | 2 | ( | 0.8% | ) | | 31 | ( | 13.1% | ) | | 31 | ( | 13.1% | ) | | 51 | ( | 21.5% | ) | | 35 | ( | 14.8% | ) | | 11 | ( | 4.6% | ) | | 2 | ( | 0.8% | ) | | 16 | ( | 6.8% | ) | | 4 | ( | 1.7% | ) | | 5 | ( | 2.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx_geocode_2000
[factor] |
1. 103
2. 107
3. 111
4. 113
5. 125
6. 163
7. 19
8. 25
9. 29
10. 59
11. 61
12. 65
13. 67
14. 71
15. 73
16. 77
17. 83
18. 95
19. 97
20. 99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
173
(99.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx_geocode_2000
[factor] |
1. 103
2. 107
3. 111
4. 113
5. 125
6. 163
7. 19
8. 25
9. 29
10. 59
11. 61
12. 65
13. 67
14. 71
15. 73
16. 77
17. 83
18. 95
19. 97
20. 99 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx_geocode_2000
[factor] |
1. 103
2. 107
3. 111
4. 113
5. 125
6. 163
7. 19
8. 25
9. 29
10. 59
11. 61
12. 65
13. 67
14. 71
15. 73
16. 77
17. 83
18. 95
19. 97
20. 99 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_dx_geocode_2000
[factor] |
1. 103
2. 107
3. 111
4. 113
5. 125
6. 163
7. 19
8. 25
9. 29
10. 59
11. 61
12. 65
13. 67
14. 71
15. 73
16. 77
17. 83
18. 95
19. 97
20. 99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 28.6% | ) | | 4 | ( | 57.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) |
|
 |
9
(56.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COUNTY AT DX GEOCODE2010
Description: County at Diagnosis 2010 Code for the county of the patient’s residence at the time the tumor was diagnosed is a derived (geocoded) variable based on Census Boundary files from 2010 Decennial Census. This code should be used for county and county-based (such as CHSDA) rates and analysis for all cases diagnosed in 2010-2019.
Rationale: Census tracts are areas geographically nested within counties and designated with a 6-digit number code. This 6-digit code is commonly repeated within a state in different counties. Census tract numbers are only unique when paired with the state and the county. Therefore, a tract cannot be accurately identified without knowing the county. Example from Massachusetts: Rural Franklin County contains a tract 040600 with 2010 population 4,612 people. Urban Suffolk County contains a tract 040600 with 2,444 people. The county must be known in order to distinguish between the two tract codes. Because we historically used a single variable for county at diagnosis [90], correct tract codes were frequently paired with the wrong county due to incorrect county assignment during abstracting or a change of county over time. Also, some variables, such as the Census Tr Poverty Indicatr [145] require the use of the decennial Census County codes closest to year of diagnosis and not the decade of year of diagnosis. Using a single county at diagnosis, and using the reported versus geocoded data, may result in erroneous assignment of geographic location as well as invalid links with census data (i.e., population, poverty category, urban/rural designation).
Instructions for Coding
- This variable is generated through the process of geocoding either during abstracting or at the central registry level.
- It is recommended that all cases diagnosed through 2019 should have a geocoded County at Diagnosis 2010.
- At a minimum, all cases diagnosed through 2006-2019 should have a geocoded County at Diagnosis 2010. Cases diagnosed 2006-2009 must have both County at Diagnosis 2000 and County at Diagnosis 2010 codes for proper assignment of the Census Tract Poverty Indicator [145]. Cases diagnosed 2016-2019 must have both County at Diagnosis 2010 and County at Diagnosis 2020 codes for proper assignment of the Census Tract Poverty Indicator [145].
- If the patient has multiple tumors, geocoded county may be different for each tumor.
- Do not update this item if the patient’s county of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a county during manual geocoding or a consolidation process.
- PO Box address information should not be used to geocode this data item except in the infrequent case when no other address information is available.
- Detailed standards have not been set for Canadian provinces/territories. Use code 998 for Canadian residents.
- Blank “Not geocoded” is allowable for cases diagnosed before 2005 and after 2019. However, it is preferred to have all cases geocoded to a 2010 Census County to allow for both retrospective and cross-sectional analyses.
Codes
- 001-997 County at diagnosis. Valid FIPS code.
- 998 Known town, city, state, or country of residence but county code not known AND a resident outside of the state of reporting institution (must meet all criteria). Use this code for Canadian residents.
- 999 County unknown. The county of the patient is unknown, or the patient is not a United States resident. County is not documented in the patient’s medical record.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#96
All data
st_css() #IMPORTANT!
countyatdxgeocode2010 <- as.factor(trimws(d[,"countyatdxgeocode2010"]))
new.d <- data.frame(new.d, countyatdxgeocode2010)
new.d <- apply_labels(new.d, countyatdxgeocode2010 = "county_at_dx_geocode_2010")
new.d.1 <- data.frame(new.d.1, countyatdxgeocode2010)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, countyatdxgeocode2010)
summarytools::view(dfSummary(new.d$countyatdxgeocode2010, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
countyatdxgeocode2010
[labelled, factor] |
county_at_dx_geocode_2010 |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 75 | ( | 3.5% | ) | | 4 | ( | 0.2% | ) | | 18 | ( | 0.8% | ) | | 10 | ( | 0.5% | ) | | 5 | ( | 0.2% | ) | | 7 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 11 | ( | 0.5% | ) | | 25 | ( | 1.2% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 5 | ( | 0.2% | ) | | 143 | ( | 6.6% | ) | | 1 | ( | 0.0% | ) | | 40 | ( | 1.8% | ) | | 11 | ( | 0.5% | ) | | 2 | ( | 0.1% | ) | | 47 | ( | 2.2% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 63 | ( | 2.9% | ) | | 1 | ( | 0.0% | ) | | 8 | ( | 0.4% | ) | | 5 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 9 | ( | 0.4% | ) | | 32 | ( | 1.5% | ) | | 9 | ( | 0.4% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 136 | ( | 6.3% | ) | | 1 | ( | 0.0% | ) | | 4 | ( | 0.2% | ) | | 18 | ( | 0.8% | ) | | 5 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 14 | ( | 0.6% | ) | | 4 | ( | 0.2% | ) | | 16 | ( | 0.7% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 6 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 26 | ( | 1.2% | ) | | 1 | ( | 0.0% | ) | | 35 | ( | 1.6% | ) | | 17 | ( | 0.8% | ) | | 1 | ( | 0.0% | ) | | 12 | ( | 0.6% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 45 | ( | 2.1% | ) | | 20 | ( | 0.9% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 9 | ( | 0.4% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 10 | ( | 0.5% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 6 | ( | 0.3% | ) | | 8 | ( | 0.4% | ) | | 1 | ( | 0.0% | ) | | 13 | ( | 0.6% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 7 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 43 | ( | 2.0% | ) | | 2 | ( | 0.1% | ) | | 192 | ( | 8.9% | ) | | 6 | ( | 0.3% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 10 | ( | 0.5% | ) | | 841 | ( | 38.8% | ) |
|
 |
1
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_d_geocode_2010
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 188 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1
(0.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_d_geocode_2010
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 73 | ( | 42.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 39 | ( | 22.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 60 | ( | 34.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_d_geocode_2010
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 4.6% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 205 | ( | 86.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_d_geocode_2010
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 34 | ( | 19.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 125 | ( | 71.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 15 | ( | 8.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_d_geocode_2010
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 1 | ( | 0.3% | ) | | 4 | ( | 1.4% | ) | | 13 | ( | 4.5% | ) | | 9 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 5 | ( | 1.7% | ) | | 4 | ( | 1.4% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 3 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 15 | ( | 5.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 39 | ( | 13.4% | ) | | 1 | ( | 0.3% | ) | | 4 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 148 | ( | 51.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_d_geocode_2010
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 2.1% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 139 | ( | 12.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 0.7% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 63 | ( | 5.8% | ) | | 1 | ( | 0.1% | ) | | 8 | ( | 0.7% | ) | | 5 | ( | 0.5% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 32 | ( | 2.9% | ) | | 9 | ( | 0.8% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 3 | ( | 0.3% | ) | | 5 | ( | 0.5% | ) | | 4 | ( | 0.4% | ) | | 3 | ( | 0.3% | ) | | 7 | ( | 0.6% | ) | | 1 | ( | 0.1% | ) | | 14 | ( | 1.3% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 6 | ( | 0.6% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 26 | ( | 2.4% | ) | | 1 | ( | 0.1% | ) | | 35 | ( | 3.2% | ) | | 17 | ( | 1.6% | ) | | 1 | ( | 0.1% | ) | | 12 | ( | 1.1% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 45 | ( | 4.1% | ) | | 20 | ( | 1.8% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 9 | ( | 0.8% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 10 | ( | 0.9% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 7 | ( | 0.6% | ) | | 6 | ( | 0.6% | ) | | 8 | ( | 0.7% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 8 | ( | 0.7% | ) | | 410 | ( | 37.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
county_at_d_geocode_2010
[factor] |
1. 1
2. 101
3. 103
4. 105
5. 107
6. 109
7. 11
8. 111
9. 113
10. 115
11. 117
12. 119
13. 121
14. 123
15. 125
16. 127
17. 129
18. 13
19. 131
20. 133
21. 135
22. 137
23. 139
24. 141
25. 145
26. 149
27. 15
28. 151
29. 153
30. 155
31. 157
32. 161
33. 163
34. 167
35. 169
36. 17
37. 171
38. 175
39. 177
40. 179
41. 183
42. 185
43. 189
44. 19
45. 191
46. 193
47. 195
48. 199
49. 201
50. 207
51. 21
52. 213
53. 215
54. 217
55. 219
56. 223
57. 225
58. 23
59. 231
60. 233
61. 235
62. 239
63. 243
64. 245
65. 247
66. 249
67. 25
68. 251
69. 253
70. 255
71. 257
72. 259
73. 261
74. 263
75. 267
76. 269
77. 27
78. 273
79. 275
80. 277
81. 285
82. 289
83. 29
84. 293
85. 297
86. 299
87. 301
88. 303
89. 305
90. 31
91. 311
92. 315
93. 321
94. 33
95. 35
96. 37
97. 39
98. 41
99. 43
100. 45
[ 30 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CENSUS TRACT 2000
Description: Identifies the patient’s census tract of residence at the time the tumor was diagnosed. Census Tract 2000 is a derived (geocoded) variables based on the Census Boundary files from 2000. See Census Tract 70/80/90 [110]; Census Tract 2010 [135]; Census Tract 2020 [125]. Codes are those used by the U.S. Census Bureau for the Year 2000 Census. For consolidated records, the geocoded state should be based on the best address at diagnosis information identified.
Rationale: Census tract codes allow central registries to calculate incidence rates for geographical areas having population estimates. This field allows a central registry to add Year 2020 Census tracts to tumors diagnosed in previous years, without losing the codes in data items [110], [130] and [135].
The Census Bureau provides population and other demographic data for census tracts. This allows for small area analysis for general surveillance or special geographical and socioeconomic analysis.
Instructions for Coding
- This variable is generated through the process of geocoding either during abstracting or at the central registry level.
- Census tract codes have a 4-digit basic number and also may have a 2-digit suffix. Census tract numbers range from 0001.00 to 9999.98, but the decimal should not be retained in the NAACCR layout.
- It is recommended that all cases diagnosed through 2009 should have a geocoded Census Tract 2000.
- At a minimum, all cases diagnosed through 1996-2009 should have a geocoded Census Tract 2000. Cases diagnosed 1996-1999 must have both State at DX Geocode 70/80/90 [82] and State at DX 2000 Geocode [83] codes for proper assignment of the Census Tr Poverty Indicatr [145]. Cases diagnosed 2006-2009 must have both State at DX 2000 Geocode [83] and State at DX Geocode 2010 [84] codes for proper assignment of the Census Tr Poverty Indicatr [145].
- If the patient has multiple tumors, geocoded state at diagnosis may be different for each tumor.
- Do not update this item if the patient’s tract of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a tract during manual geocoding or a consolidation process.
- PO Box address information should not be used to geocode this data item except in the infrequent case when no other address information is available.
- Blank “Not geocoded” is allowable for cases diagnosed before 1995 and after 2009. However, it is recommended to have all cases geocoded to a 2000 Census Tract to allow for both retrospective and cross-sectional analyses.
Codes
- 000100-999998 Valid FIPS code
- 000000 Area not census tracted
- 999999 Area census-tracted, but census tract is not available
- Blank Census Tract 2000 not coded
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#130
All data
st_css() #IMPORTANT!
censustract2000 <- as.factor(trimws(d[,"censustract2000"]))
levels(censustract2000)[levels(censustract2000)=="999999"] <- "not_available.999999"
new.d <- data.frame(new.d, censustract2000)
new.d <- apply_labels(new.d, censustract2000 = "census_tract_2000")
new.d.1 <- data.frame(new.d.1, censustract2000)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, censustract2000)
summarytools::view(dfSummary(new.d$censustract2000, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
censustract2000
[labelled, factor] |
census_tract_2000 |
1. 160800
2. 166700
3. 255200
4. 30501
5. 521900
6. 524800
7. 538900
8. 541000
9. 561900
10. not_available.999999 |
| 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 599 | ( | 98.5% | ) |
|
 |
1559
(71.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2000
[factor] |
1. 160800
2. 166700
3. 255200
4. 30501
5. 521900
6. 524800
7. 538900
8. 541000
9. 561900
10. not_available.999999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 189 | ( | 100.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2000
[factor] |
1. 160800
2. 166700
3. 255200
4. 30501
5. 521900
6. 524800
7. 538900
8. 541000
9. 561900
10. not_available.999999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 174 | ( | 100.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2000
[factor] |
1. 160800
2. 166700
3. 255200
4. 30501
5. 521900
6. 524800
7. 538900
8. 541000
9. 561900
10. not_available.999999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 236 | ( | 99.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2000
[factor] |
1. 160800
2. 166700
3. 255200
4. 30501
5. 521900
6. 524800
7. 538900
8. 541000
9. 561900
10. not_available.999999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
173
(99.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2000
[factor] |
1. 160800
2. 166700
3. 255200
4. 30501
5. 521900
6. 524800
7. 538900
8. 541000
9. 561900
10. not_available.999999 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2000
[factor] |
1. 160800
2. 166700
3. 255200
4. 30501
5. 521900
6. 524800
7. 538900
8. 541000
9. 561900
10. not_available.999999 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2000
[factor] |
1. 160800
2. 166700
3. 255200
4. 30501
5. 521900
6. 524800
7. 538900
8. 541000
9. 561900
10. not_available.999999 |
| 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) |
|
 |
9
(56.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CENSUS TRACT 2010
Description: Identifies the patient’s census tract of residence at the time the tumor was diagnosed. Census Tract 2010 is a derived (geocoded) variables based on the Census Boundary files from 2010. See Census Tract 1970/80/90 [110]; Census Tract 2000 [130]; Census Tract 2020 [125]. Codes are those used by the U.S. Census Bureau for the Year 2010 Census. For consolidated records, the geocoded state should be based on the best address at diagnosis information identified.
Rationale: Census tract codes allow central registries to calculate incidence rates for geographical areas having population estimates. This field allows a central registry to add Year 2020 Census tracts to tumors diagnosed in previous years, without losing the codes in data items [110], [130] and [135].
The Census Bureau provides population and other demographic data for census tracts. This allows for small area analysis for general surveillance or special geographical and socioeconomic analysis.
Instructions for Coding
- This variable is generated through the process of geocoding either during abstracting or at the central registry level.
- Census tract codes have a 4-digit basic number and also may have a 2-digit suffix. Census tract numbers range from 0001.00 to 9999.98, but the decimal should not be retained in the NAACCR layout.
- It is recommended that all cases diagnosed through 2019 should have a geocoded Census Tract 2010.
- At a minimum, all cases diagnosed through 2006-2019 should have a geocoded Census Tract 2010. Cases diagnosed 2006-2009 must have both State at DX Geocode 2000 [82] and State at DX Geocode 2010 [83] codes for proper assignment of the Census Tr Poverty Indicatr [145]. Cases diagnosed 2016-2019 must have both State at DX Geocode 2010 [83] and State at DX Geocode 2020 [84] codes for proper assignment of the Census Tr Poverty Indicatr [145].
- If the patient has multiple tumors, geocoded state at diagnosis may be different for each tumor.
- Do not update this item if the patient’s tract of residence changes. Store updated address information in the affiliated current address data items. Only update based on improved information on the residential address at time of diagnosis. For instance, it is appropriate to correct a tract during manual geocoding or a consolidation process.
- PO Box address information should not be used to geocode this data item except in the infrequent case when no other address information is available.
- Blank “Not geocoded” is allowable for cases diagnosed before 2005 and after 2019. However, it is preferred to have all cases geocoded to a 2010 Census Tract to allow for both retrospective and cross-sectional analyses.
Codes
- 000100-999998 Valid FIPS code
- 000000 Area not census tracted
- 999999 Area census tracted, but census tract is not available
- Blank Census Tract 2010 not coded
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#135
All data
st_css() #IMPORTANT!
censustract2010 <- as.factor(trimws(d[,"censustract2010"]))
levels(censustract2010)[levels(censustract2010)=="999999"] <- "not_available.999999"
new.d <- data.frame(new.d, censustract2010)
new.d <- apply_labels(new.d, censustract2010 = "census_tract_2010")
new.d.1 <- data.frame(new.d.1, censustract2010)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, censustract2010)
summarytools::view(dfSummary(new.d$censustract2010, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
censustract2010
[labelled, factor] |
census_tract_2010 |
1. 100
2. 1000
3. 10017
4. 10023
5. 1003
6. 100300
7. 100501
8. 100502
9. 100600
10. 100700
11. 100901
12. 100902
13. 100903
14. 10100
15. 10103
16. 10106
17. 10107
18. 10110
19. 102
20. 10200
21. 10203
22. 10204
23. 10205
24. 10206
25. 10208
26. 10300
27. 10301
28. 10302
29. 10303
30. 10304
31. 10400
32. 10401
33. 10402
34. 10403
35. 104101
36. 10416
37. 10422
38. 10500
39. 105000
40. 10501
41. 10502
42. 10504
43. 10507
44. 10508
45. 10510
46. 10511
47. 10512
48. 10513
49. 10514
50. 10515
51. 10516
52. 10600
53. 10601
54. 10602
55. 10603
56. 10604
57. 10605
58. 10607
59. 10608
60. 106403
61. 106646
62. 10700
63. 10701
64. 10702
65. 10703
66. 10706
67. 10707
68. 10708
69. 10709
70. 10710
71. 10712
72. 10800
73. 10801
74. 10802
75. 10803
76. 10806
77. 10807
78. 10808
79. 10900
80. 10903
81. 10904
82. 10906
83. 109800
84. 1100
85. 11000
86. 11003
87. 110115
88. 110200
89. 1103
90. 110300
91. 110301
92. 110400
93. 110503
94. 110505
95. 110601
96. 110700
97. 110900
98. 11100
99. 11102
100. 11105
[ 1242 others ] |
| 7 | ( | 0.3% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 8 | ( | 0.4% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) | | 6 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 9 | ( | 0.4% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 5 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) | | 5 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 1924 | ( | 88.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2010
[factor] |
1. 100
2. 1000
3. 10017
4. 10023
5. 1003
6. 100300
7. 100501
8. 100502
9. 100600
10. 100700
11. 100901
12. 100902
13. 100903
14. 10100
15. 10103
16. 10106
17. 10107
18. 10110
19. 102
20. 10200
21. 10203
22. 10204
23. 10205
24. 10206
25. 10208
26. 10300
27. 10301
28. 10302
29. 10303
30. 10304
31. 10400
32. 10401
33. 10402
34. 10403
35. 104101
36. 10416
37. 10422
38. 10500
39. 105000
40. 10501
41. 10502
42. 10504
43. 10507
44. 10508
45. 10510
46. 10511
47. 10512
48. 10513
49. 10514
50. 10515
51. 10516
52. 10600
53. 10601
54. 10602
55. 10603
56. 10604
57. 10605
58. 10607
59. 10608
60. 106403
61. 106646
62. 10700
63. 10701
64. 10702
65. 10703
66. 10706
67. 10707
68. 10708
69. 10709
70. 10710
71. 10712
72. 10800
73. 10801
74. 10802
75. 10803
76. 10806
77. 10807
78. 10808
79. 10900
80. 10903
81. 10904
82. 10906
83. 109800
84. 1100
85. 11000
86. 11003
87. 110115
88. 110200
89. 1103
90. 110300
91. 110301
92. 110400
93. 110503
94. 110505
95. 110601
96. 110700
97. 110900
98. 11100
99. 11102
100. 11105
[ 1242 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 186 | ( | 98.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2010
[factor] |
1. 100
2. 1000
3. 10017
4. 10023
5. 1003
6. 100300
7. 100501
8. 100502
9. 100600
10. 100700
11. 100901
12. 100902
13. 100903
14. 10100
15. 10103
16. 10106
17. 10107
18. 10110
19. 102
20. 10200
21. 10203
22. 10204
23. 10205
24. 10206
25. 10208
26. 10300
27. 10301
28. 10302
29. 10303
30. 10304
31. 10400
32. 10401
33. 10402
34. 10403
35. 104101
36. 10416
37. 10422
38. 10500
39. 105000
40. 10501
41. 10502
42. 10504
43. 10507
44. 10508
45. 10510
46. 10511
47. 10512
48. 10513
49. 10514
50. 10515
51. 10516
52. 10600
53. 10601
54. 10602
55. 10603
56. 10604
57. 10605
58. 10607
59. 10608
60. 106403
61. 106646
62. 10700
63. 10701
64. 10702
65. 10703
66. 10706
67. 10707
68. 10708
69. 10709
70. 10710
71. 10712
72. 10800
73. 10801
74. 10802
75. 10803
76. 10806
77. 10807
78. 10808
79. 10900
80. 10903
81. 10904
82. 10906
83. 109800
84. 1100
85. 11000
86. 11003
87. 110115
88. 110200
89. 1103
90. 110300
91. 110301
92. 110400
93. 110503
94. 110505
95. 110601
96. 110700
97. 110900
98. 11100
99. 11102
100. 11105
[ 1242 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 170 | ( | 97.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2010
[factor] |
1. 100
2. 1000
3. 10017
4. 10023
5. 1003
6. 100300
7. 100501
8. 100502
9. 100600
10. 100700
11. 100901
12. 100902
13. 100903
14. 10100
15. 10103
16. 10106
17. 10107
18. 10110
19. 102
20. 10200
21. 10203
22. 10204
23. 10205
24. 10206
25. 10208
26. 10300
27. 10301
28. 10302
29. 10303
30. 10304
31. 10400
32. 10401
33. 10402
34. 10403
35. 104101
36. 10416
37. 10422
38. 10500
39. 105000
40. 10501
41. 10502
42. 10504
43. 10507
44. 10508
45. 10510
46. 10511
47. 10512
48. 10513
49. 10514
50. 10515
51. 10516
52. 10600
53. 10601
54. 10602
55. 10603
56. 10604
57. 10605
58. 10607
59. 10608
60. 106403
61. 106646
62. 10700
63. 10701
64. 10702
65. 10703
66. 10706
67. 10707
68. 10708
69. 10709
70. 10710
71. 10712
72. 10800
73. 10801
74. 10802
75. 10803
76. 10806
77. 10807
78. 10808
79. 10900
80. 10903
81. 10904
82. 10906
83. 109800
84. 1100
85. 11000
86. 11003
87. 110115
88. 110200
89. 1103
90. 110300
91. 110301
92. 110400
93. 110503
94. 110505
95. 110601
96. 110700
97. 110900
98. 11100
99. 11102
100. 11105
[ 1242 others ] |
| 1 | ( | 0.4% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 225 | ( | 94.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2010
[factor] |
1. 100
2. 1000
3. 10017
4. 10023
5. 1003
6. 100300
7. 100501
8. 100502
9. 100600
10. 100700
11. 100901
12. 100902
13. 100903
14. 10100
15. 10103
16. 10106
17. 10107
18. 10110
19. 102
20. 10200
21. 10203
22. 10204
23. 10205
24. 10206
25. 10208
26. 10300
27. 10301
28. 10302
29. 10303
30. 10304
31. 10400
32. 10401
33. 10402
34. 10403
35. 104101
36. 10416
37. 10422
38. 10500
39. 105000
40. 10501
41. 10502
42. 10504
43. 10507
44. 10508
45. 10510
46. 10511
47. 10512
48. 10513
49. 10514
50. 10515
51. 10516
52. 10600
53. 10601
54. 10602
55. 10603
56. 10604
57. 10605
58. 10607
59. 10608
60. 106403
61. 106646
62. 10700
63. 10701
64. 10702
65. 10703
66. 10706
67. 10707
68. 10708
69. 10709
70. 10710
71. 10712
72. 10800
73. 10801
74. 10802
75. 10803
76. 10806
77. 10807
78. 10808
79. 10900
80. 10903
81. 10904
82. 10906
83. 109800
84. 1100
85. 11000
86. 11003
87. 110115
88. 110200
89. 1103
90. 110300
91. 110301
92. 110400
93. 110503
94. 110505
95. 110601
96. 110700
97. 110900
98. 11100
99. 11102
100. 11105
[ 1242 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 174 | ( | 100.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2010
[factor] |
1. 100
2. 1000
3. 10017
4. 10023
5. 1003
6. 100300
7. 100501
8. 100502
9. 100600
10. 100700
11. 100901
12. 100902
13. 100903
14. 10100
15. 10103
16. 10106
17. 10107
18. 10110
19. 102
20. 10200
21. 10203
22. 10204
23. 10205
24. 10206
25. 10208
26. 10300
27. 10301
28. 10302
29. 10303
30. 10304
31. 10400
32. 10401
33. 10402
34. 10403
35. 104101
36. 10416
37. 10422
38. 10500
39. 105000
40. 10501
41. 10502
42. 10504
43. 10507
44. 10508
45. 10510
46. 10511
47. 10512
48. 10513
49. 10514
50. 10515
51. 10516
52. 10600
53. 10601
54. 10602
55. 10603
56. 10604
57. 10605
58. 10607
59. 10608
60. 106403
61. 106646
62. 10700
63. 10701
64. 10702
65. 10703
66. 10706
67. 10707
68. 10708
69. 10709
70. 10710
71. 10712
72. 10800
73. 10801
74. 10802
75. 10803
76. 10806
77. 10807
78. 10808
79. 10900
80. 10903
81. 10904
82. 10906
83. 109800
84. 1100
85. 11000
86. 11003
87. 110115
88. 110200
89. 1103
90. 110300
91. 110301
92. 110400
93. 110503
94. 110505
95. 110601
96. 110700
97. 110900
98. 11100
99. 11102
100. 11105
[ 1242 others ] |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 278 | ( | 95.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2010
[factor] |
1. 100
2. 1000
3. 10017
4. 10023
5. 1003
6. 100300
7. 100501
8. 100502
9. 100600
10. 100700
11. 100901
12. 100902
13. 100903
14. 10100
15. 10103
16. 10106
17. 10107
18. 10110
19. 102
20. 10200
21. 10203
22. 10204
23. 10205
24. 10206
25. 10208
26. 10300
27. 10301
28. 10302
29. 10303
30. 10304
31. 10400
32. 10401
33. 10402
34. 10403
35. 104101
36. 10416
37. 10422
38. 10500
39. 105000
40. 10501
41. 10502
42. 10504
43. 10507
44. 10508
45. 10510
46. 10511
47. 10512
48. 10513
49. 10514
50. 10515
51. 10516
52. 10600
53. 10601
54. 10602
55. 10603
56. 10604
57. 10605
58. 10607
59. 10608
60. 106403
61. 106646
62. 10700
63. 10701
64. 10702
65. 10703
66. 10706
67. 10707
68. 10708
69. 10709
70. 10710
71. 10712
72. 10800
73. 10801
74. 10802
75. 10803
76. 10806
77. 10807
78. 10808
79. 10900
80. 10903
81. 10904
82. 10906
83. 109800
84. 1100
85. 11000
86. 11003
87. 110115
88. 110200
89. 1103
90. 110300
91. 110301
92. 110400
93. 110503
94. 110505
95. 110601
96. 110700
97. 110900
98. 11100
99. 11102
100. 11105
[ 1242 others ] |
| 6 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 8 | ( | 0.7% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 5 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 7 | ( | 0.6% | ) | | 8 | ( | 0.7% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 0.5% | ) | | 5 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 875 | ( | 80.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tract_2010
[factor] |
1. 100
2. 1000
3. 10017
4. 10023
5. 1003
6. 100300
7. 100501
8. 100502
9. 100600
10. 100700
11. 100901
12. 100902
13. 100903
14. 10100
15. 10103
16. 10106
17. 10107
18. 10110
19. 102
20. 10200
21. 10203
22. 10204
23. 10205
24. 10206
25. 10208
26. 10300
27. 10301
28. 10302
29. 10303
30. 10304
31. 10400
32. 10401
33. 10402
34. 10403
35. 104101
36. 10416
37. 10422
38. 10500
39. 105000
40. 10501
41. 10502
42. 10504
43. 10507
44. 10508
45. 10510
46. 10511
47. 10512
48. 10513
49. 10514
50. 10515
51. 10516
52. 10600
53. 10601
54. 10602
55. 10603
56. 10604
57. 10605
58. 10607
59. 10608
60. 106403
61. 106646
62. 10700
63. 10701
64. 10702
65. 10703
66. 10706
67. 10707
68. 10708
69. 10709
70. 10710
71. 10712
72. 10800
73. 10801
74. 10802
75. 10803
76. 10806
77. 10807
78. 10808
79. 10900
80. 10903
81. 10904
82. 10906
83. 109800
84. 1100
85. 11000
86. 11003
87. 110115
88. 110200
89. 1103
90. 110300
91. 110301
92. 110400
93. 110503
94. 110505
95. 110601
96. 110700
97. 110900
98. 11100
99. 11102
100. 11105
[ 1242 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 16 | ( | 100.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
MARITAL STATUS AT DX
- Description: Code for the patient’s marital status at the time of diagnosis for the reportable tumor. If the patient has multiple tumors, marital status may be different for each tumor.
- Rationale: Incidence and survival with certain cancers vary by marital status. The item also helps in patient identification.
- Codes
- 1 Single (never married)
- 2 Married (including common law)
- 3 Separated
- 4 Divorced
- 5 Widowed
- 6 Unmarried or Domestic Partner (same sex or opposite sex, registered or unregistered, other than common law marriage)
- 9 Unknown
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#150
All data
st_css() #IMPORTANT!
maritalstatusatdx <- as.factor(trimws(d[,"maritalstatusatdx"]))
levels(maritalstatusatdx)[levels(maritalstatusatdx)=="1"] <- "Single.1"
levels(maritalstatusatdx)[levels(maritalstatusatdx)=="2"] <- "Married.2"
levels(maritalstatusatdx)[levels(maritalstatusatdx)=="3"] <- "Separated.3"
levels(maritalstatusatdx)[levels(maritalstatusatdx)=="4"] <- "Divorced.4"
levels(maritalstatusatdx)[levels(maritalstatusatdx)=="5"] <- "Widowed.5"
levels(maritalstatusatdx)[levels(maritalstatusatdx)=="6"] <- "Unmarried_or_Domestic_Partner.6"
levels(maritalstatusatdx)[levels(maritalstatusatdx)=="9"] <- "Unknown.9"
new.d <- data.frame(new.d, maritalstatusatdx)
new.d <- apply_labels(new.d, maritalstatusatdx = "marital_status_at_dx")
new.d.1 <- data.frame(new.d.1, maritalstatusatdx)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, maritalstatusatdx)
summarytools::view(dfSummary(new.d$maritalstatusatdx, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
maritalstatusatdx
[labelled, factor] |
marital_status_at_dx |
1. Single.1
2. Married.2
3. Separated.3
4. Divorced.4
5. Widowed.5
6. Unmarried_or_Domestic_Par
7. Unknown.9 |
| 399 | ( | 18.4% | ) | | 1264 | ( | 58.3% | ) | | 40 | ( | 1.8% | ) | | 176 | ( | 8.1% | ) | | 52 | ( | 2.4% | ) | | 9 | ( | 0.4% | ) | | 227 | ( | 10.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
marital_status_at_dx
[factor] |
1. Single.1
2. Married.2
3. Separated.3
4. Divorced.4
5. Widowed.5
6. Unmarried_or_Domestic_Par
7. Unknown.9 |
| 53 | ( | 28.0% | ) | | 98 | ( | 51.9% | ) | | 5 | ( | 2.6% | ) | | 17 | ( | 9.0% | ) | | 4 | ( | 2.1% | ) | | 1 | ( | 0.5% | ) | | 11 | ( | 5.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
marital_status_at_dx
[factor] |
1. Single.1
2. Married.2
3. Separated.3
4. Divorced.4
5. Widowed.5
6. Unmarried_or_Domestic_Par
7. Unknown.9 |
| 44 | ( | 25.3% | ) | | 94 | ( | 54.0% | ) | | 1 | ( | 0.6% | ) | | 12 | ( | 6.9% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 20 | ( | 11.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
marital_status_at_dx
[factor] |
1. Single.1
2. Married.2
3. Separated.3
4. Divorced.4
5. Widowed.5
6. Unmarried_or_Domestic_Par
7. Unknown.9 |
| 40 | ( | 16.9% | ) | | 149 | ( | 62.9% | ) | | 2 | ( | 0.8% | ) | | 13 | ( | 5.5% | ) | | 4 | ( | 1.7% | ) | | 4 | ( | 1.7% | ) | | 25 | ( | 10.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
marital_status_at_dx
[factor] |
1. Single.1
2. Married.2
3. Separated.3
4. Divorced.4
5. Widowed.5
6. Unmarried_or_Domestic_Par
7. Unknown.9 |
| 44 | ( | 25.3% | ) | | 83 | ( | 47.7% | ) | | 6 | ( | 3.4% | ) | | 21 | ( | 12.1% | ) | | 5 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 15 | ( | 8.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
marital_status_at_dx
[factor] |
1. Single.1
2. Married.2
3. Separated.3
4. Divorced.4
5. Widowed.5
6. Unmarried_or_Domestic_Par
7. Unknown.9 |
| 60 | ( | 20.7% | ) | | 170 | ( | 58.6% | ) | | 5 | ( | 1.7% | ) | | 25 | ( | 8.6% | ) | | 7 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 7.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
marital_status_at_dx
[factor] |
1. Single.1
2. Married.2
3. Separated.3
4. Divorced.4
5. Widowed.5
6. Unmarried_or_Domestic_Par
7. Unknown.9 |
| 155 | ( | 14.3% | ) | | 661 | ( | 60.8% | ) | | 21 | ( | 1.9% | ) | | 86 | ( | 7.9% | ) | | 28 | ( | 2.6% | ) | | 3 | ( | 0.3% | ) | | 133 | ( | 12.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
marital_status_at_dx
[factor] |
1. Single.1
2. Married.2
3. Separated.3
4. Divorced.4
5. Widowed.5
6. Unmarried_or_Domestic_Par
7. Unknown.9 |
| 3 | ( | 18.8% | ) | | 9 | ( | 56.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 12.5% | ) | | 2 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RACE 1
Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.
Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G).
Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories.
Codes
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#160
All data
st_css() #IMPORTANT!
race1 <- as.factor(trimws(d[,"race1"]))
levels(race1)[levels(race1)=="1"] <- "White.1"
levels(race1)[levels(race1)=="2"] <- "Black.2"
new.d <- data.frame(new.d, race1)
new.d <- apply_labels(new.d, race1 = "race_1")
new.d.1 <- data.frame(new.d.1, race1)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, race1)
summarytools::view(dfSummary(new.d$race1, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race1
[labelled, factor] |
race_1 |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race1
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race1
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race1
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race1
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race1
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race1
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race1
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RACE 2
-Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.
-Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G). -Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories. - Codes + 01 White + 02 Black + 88 No further race documented
All data
st_css() #IMPORTANT!
race2 <- as.factor(trimws(d[,"race2"]))
levels(race2)[levels(race2)=="1"] <- "White.1"
levels(race2)[levels(race2)=="88"] <- "No_further_race.88"
new.d <- data.frame(new.d, race2)
new.d <- apply_labels(new.d, race2 = "race_2")
new.d.1 <- data.frame(new.d.1, race2)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, race2)
summarytools::view(dfSummary(new.d$race2, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race2
[labelled, factor] |
race_2 |
1. White.1
2. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race2
[factor] |
1. White.1
2. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race2
[factor] |
1. White.1
2. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race2
[factor] |
1. White.1
2. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race2
[factor] |
1. White.1
2. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race2
[factor] |
1. White.1
2. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race2
[factor] |
1. White.1
2. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race2
[factor] |
1. White.1
2. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RACE 3
Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.
Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G).
Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories.
Codes
- 01 White
- 02 Black
- 88 No further race documented
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#162
All data
st_css() #IMPORTANT!
race3 <- as.factor(trimws(d[,"race3"]))
levels(race3)[levels(race3)=="1"] <- "White.1"
levels(race3)[levels(race3)=="88"] <- "No_further_race.88"
new.d <- data.frame(new.d, race3)
new.d <- apply_labels(new.d, race3 = "race_3")
new.d.1 <- data.frame(new.d.1, race3)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, race3)
summarytools::view(dfSummary(new.d$race3, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race3
[labelled, factor] |
race_3 |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race3
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race3
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race3
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race3
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race3
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race3
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race3
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RACE 4
Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.
Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G).
Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories.
Codes
- 01 White
- 02 Black
- 88 No further race documented
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#163
All data
st_css() #IMPORTANT!
race4 <- as.factor(trimws(d[,"race4"]))
levels(race4)[levels(race4)=="1"] <- "White.1"
levels(race4)[levels(race4)=="88"] <- "No_further_race.88"
new.d <- data.frame(new.d, race4)
new.d <- apply_labels(new.d, race4 = "race_4")
new.d.1 <- data.frame(new.d.1, race4)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, race4)
summarytools::view(dfSummary(new.d$race4, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race4
[labelled, factor] |
race_4 |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race4
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race4
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race4
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race4
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race4
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race4
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race4
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RACE 5
Description: Code the patient’s race. Race is coded separately from Spanish/Hispanic Origin [190]. All tumors for the same patient should have the same race codes. If the patient is multiracial, code all races using RACE 2 through RACE 5 [161-164]. For coding instructions and race code history see the current SEER Program Coding and Staging Manual3.
Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf (Appendix G).
Rationale: Because race has a significant association with cancer rates and outcomes, a comparison between areas with different racial distributions may require an analysis of race to interpret the findings. The race codes listed correspond closely to race categories used by the U.S. Census Bureau to allow calculation of race-specific incidence rates. The full coding system should be used to allow accurate national comparison and collaboration, even if the state population does not include many of the race categories.
Codes
- 01 White
- 02 Black
- 88 No further race documented
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#164
All data
st_css() #IMPORTANT!
race5 <- as.factor(trimws(d[,"race5"]))
levels(race5)[levels(race5)=="1"] <- "White.1"
levels(race5)[levels(race5)=="88"] <- "No_further_race.88"
new.d <- data.frame(new.d, race5)
new.d <- apply_labels(new.d, race5 = "race_5")
new.d.1 <- data.frame(new.d.1, race5)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, race5)
summarytools::view(dfSummary(new.d$race5, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race5
[labelled, factor] |
race_5 |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race5
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race5
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race5
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race5
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race5
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race5
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race5
[factor] |
1. No_further_race.88 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SPANISH/HISPANIC ORIGIN
- Description: Code identifying persons of Spanish or Hispanic origin. This code is used by hospital and central registries to show the “best guess” as to whether or not the person should be classified as Hispanic for purposes of calculating cancer rates. If the patient has multiple tumors, all records should have the same code.
- Reference to Census 2000 definitions for ethnicity and race: http://www.census.gov/prod/cen2000/doc/sf2.pdf. All information resources should be used to determine the correct code, including:
- Stated ethnicity in the medical record
- Stated Hispanic origin on the death certificate
- Birthplace
- Information about life history and/or language spoken found during the abstracting process
- Patient’s last name [2230] or maiden name [2390] found on a list of Hispanic names
- Some registries code the information from the medical record, others code ethnicity based on Spanish names, and others use a combination of methods.
- Persons of Spanish or Hispanic origin may be of any race, but these categories generally are not used for Native Americans, Filipinos, etc., who may have Spanish names. If a patient has an Hispanic name, but there is reason to believe they are not Hispanic (e.g., the patient is Filipino, or the patient is a woman known to be non-Hispanic who has a Hispanic married name), the code in this field should be 0 (non-Spanish, non-Hispanic). The code in item Computed Ethnicity [200], however, would reflect the Hispanic name.
- Assign code 7 if Hispanic ethnicity is based strictly on a computer list or algorithm (unless contrary evidence is available) and also code in Computed Ethnicity [200].
- Rationale: See the rationales for the Race 1-5 [160-164] and Computed Ethnicity [200]. Ethnic origin has a significant association with cancer rates and outcomes. Hispanic populations have different patterns of occurrence of cancer from other populations that may be included in the “white” category of Race [160].
- Codes
- 0 Non-Spanish; non-Hispanic
- 1 Mexican (includes Chicano)
- 2 Puerto Rican
- 3 Cuban
- 4 South or Central American (except Brazil)
- 5 Other specified Spanish/Hispanic origin (includes European; excludes Dominican Republic)
- 6 Spanish, NOS Hispanic, NOS Latino, NOS There is evidence, other than surname or maiden name, that the person is Hispanic, but he/she cannot be assigned to any of the other categories 1-5.
- 7 Spanish surname only (Code 7 is ordinarily for central registry use only, hospital registrars may use code 7 if using a list of Hispanic surnames provided by their central registry; otherwise, code 9 ‘unknown whether Spanish or not’ should be used.) The only evidence of the person’s Hispanic origin is the surname or maiden name and there is no contrary evidence that the person is not Hispanic.
- 8 Dominican Republic
- 9 Unknown whether Spanish or not
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#190
All data
st_css() #IMPORTANT!
spanishhispanicorigin <- as.factor(trimws(d[,"spanishhispanicorigin"]))
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="0"] <- "Non_Spanish_Hispanic.0"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="1"] <- "Mexican.1"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="2"] <- "Puerto_Rican.2"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="3"] <- "Cuban.3"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="4"] <- "South_or_Central_American.4)"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="5"] <- "Other_specified.5"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="6"] <- "Spanish_Hispanic_Latino_NOS.6"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="7"] <- "Spanish_surname_only.7"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="8"] <- "Dominican_Republic.8"
levels(spanishhispanicorigin)[levels(spanishhispanicorigin)=="9"] <- "Unknown.9"
new.d <- data.frame(new.d, spanishhispanicorigin)
new.d <- apply_labels(new.d, spanishhispanicorigin = "spanish_hispanic_origin")
new.d.1 <- data.frame(new.d.1, spanishhispanicorigin)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, spanishhispanicorigin)
summarytools::view(dfSummary(new.d$spanishhispanicorigin, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
spanishhispanicorigin
[labelled, factor] |
spanish_hispanic_origin |
1. Non_Spanish_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. Dominican_Republic.8
7. Unknown.9 |
| 2145 | ( | 99.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 10 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
spanish_hispanic_origin
[factor] |
1. Non_Spanish_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. Dominican_Republic.8
7. Unknown.9 |
| 187 | ( | 98.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
spanish_hispanic_origin
[factor] |
1. Non_Spanish_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. Dominican_Republic.8
7. Unknown.9 |
| 172 | ( | 98.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
spanish_hispanic_origin
[factor] |
1. Non_Spanish_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. Dominican_Republic.8
7. Unknown.9 |
| 235 | ( | 99.2% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
spanish_hispanic_origin
[factor] |
1. Non_Spanish_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. Dominican_Republic.8
7. Unknown.9 |
| 173 | ( | 99.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
spanish_hispanic_origin
[factor] |
1. Non_Spanish_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. Dominican_Republic.8
7. Unknown.9 |
| 283 | ( | 97.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 6 | ( | 2.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
spanish_hispanic_origin
[factor] |
1. Non_Spanish_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. Dominican_Republic.8
7. Unknown.9 |
| 1079 | ( | 99.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
spanish_hispanic_origin
[factor] |
1. Non_Spanish_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. Dominican_Republic.8
7. Unknown.9 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
NHIA DERIVED HISP ORIGIN
Description: The NAACCR Hispanic Identification Algorithm (NHIA) uses a combination of standard variables to directly or indirectly classify cases as Hispanic for analytic purposes. It is possible to separate Hispanic ancestral subgroups (e.g., Mexican) when indirect assignment results from birthplace information but not from surname match. The algorithm uses the following standard variables: Spanish/Hispanic Origin [190], Name–Last [2230], Name–Maiden [2390], Birthplace [250], Race 1 [160], IHS Link [192], and Sex [220].
Code 7 (Spanish surname only) of the Spanish/Hispanic Origin [190] data item became effective with 1994 diagnoses. It is recommended that NHIA should be run on 1995 and later diagnoses. However, a central registry may run it on their data for prior years. For greater detail, please refer to the technical documentation: http://www.naaccr.org/LinkClick.aspx?fileticket=6E20OT41TcA%3d&tabid=118&mid=458.
Rationale: Sometimes despite best efforts to obtain complete information directly from the medical record, information is not available and is reported to the cancer registry as a missing data item. With regard to Hispanic ethnicity, some cancer registries have found it necessary to rely on indirect methods to populate this data element. Registries often have significant numbers or proportions of Hispanic populations in their jurisdiction.
Codes
- 0 Non-Hispanic
- 1 Mexican, by birthplace or other specific identifier
- 2 Puerto Rican, by birthplace or other specific identifier
- 3 Cuban, by birthplace or other specific identifier
- 4 South or Central American (except Brazil), by birthplace or other specific identifier
- 5 Other specified Spanish/Hispanic origin (includes European; excludes Dominican Republic), by birthplace or other specific identifier
- 6 Spanish, NOS; Hispanic, NOS; Latino, NOS
- 7 NHIA surname match only
- 8 Dominican Republic
- Blank Algorithm has not been run
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#191
All data
st_css() #IMPORTANT!
nhiaderivedhisporigin <- as.factor(trimws(d[,"nhiaderivedhisporigin"]))
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="0"] <- "Non_Hispanic.0"
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="1"] <- "Mexican.1"
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="2"] <- "Puerto_Rican.2"
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="3"] <- "Cuban.3"
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="4"] <- "South_or_Central_American.4)"
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="5"] <- "Other_specified.5"
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="6"] <- "Spanish_Hispanic_Latino_NOS.6"
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="7"] <- "NHIA_surname_match.7"
levels(nhiaderivedhisporigin)[levels(nhiaderivedhisporigin)=="8"] <- "Dominican_Republic.8"
new.d <- data.frame(new.d, nhiaderivedhisporigin)
new.d <- apply_labels(new.d, nhiaderivedhisporigin = "nhia_derived_hisp_origin")
new.d.1 <- data.frame(new.d.1, nhiaderivedhisporigin)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, nhiaderivedhisporigin)
summarytools::view(dfSummary(new.d$nhiaderivedhisporigin, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
nhiaderivedhisporigin
[labelled, factor] |
nhia_derived_hisp_origin |
1. Non_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. NHIA_surname_match.7
7. Dominican_Republic.8 |
| 2152 | ( | 99.3% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
nhia_derived_hisp_origin
[factor] |
1. Non_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. NHIA_surname_match.7
7. Dominican_Republic.8 |
| 186 | ( | 98.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
nhia_derived_hisp_origin
[factor] |
1. Non_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. NHIA_surname_match.7
7. Dominican_Republic.8 |
| 171 | ( | 98.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
nhia_derived_hisp_origin
[factor] |
1. Non_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. NHIA_surname_match.7
7. Dominican_Republic.8 |
| 235 | ( | 99.2% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
nhia_derived_hisp_origin
[factor] |
1. Non_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. NHIA_surname_match.7
7. Dominican_Republic.8 |
| 173 | ( | 99.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
nhia_derived_hisp_origin
[factor] |
1. Non_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. NHIA_surname_match.7
7. Dominican_Republic.8 |
| 289 | ( | 99.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
nhia_derived_hisp_origin
[factor] |
1. Non_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. NHIA_surname_match.7
7. Dominican_Republic.8 |
| 1082 | ( | 99.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
nhia_derived_hisp_origin
[factor] |
1. Non_Hispanic.0
2. Cuban.3
3. South_or_Central_American
4. Other_specified.5
5. Spanish_Hispanic_Latino_N
6. NHIA_surname_match.7
7. Dominican_Republic.8 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
IHS LINK
- Description: This variable captures the results of the linkage of the registry database with the Indian Health Service patient registration database.
- Rationale: The IHS linkage identifies cancer cases among American Indians who were misclassified as non-Indian in the registry database in order to improve the quality of cancer surveillance data on American Indians in individual registries and in all registries as a whole. The goal is to include cancer incidence data for American Indians in the United States Cancer Statistics by use of this variable as well as the race variable.
- Codes
- 0 Record sent for linkage, no IHS match
- 1 Record sent for linkage, IHS match
- Blank Record not sent for linkage or linkage result pending
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#192
All data
st_css() #IMPORTANT!
ihslink <- as.factor(trimws(d[,"ihslink"]))
levels(ihslink)[levels(ihslink)=="0"] <- "no_IHS_match.0"
levels(ihslink)[levels(ihslink)=="1"] <- "IHS_match.1"
new.d <- data.frame(new.d, ihslink)
new.d <- apply_labels(new.d, ihslink = "ihs_link")
new.d.1 <- data.frame(new.d.1, ihslink)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, ihslink)
summarytools::view(dfSummary(new.d$ihslink, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ihslink
[labelled, factor] |
ihs_link |
1. no_IHS_match.0
2. IHS_match.1 |
|
 |
768
(35.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ihs_link
[factor] |
1. no_IHS_match.0
2. IHS_match.1 |
|
 |
184
(97.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ihs_link
[factor] |
1. no_IHS_match.0
2. IHS_match.1 |
|
 |
168
(96.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ihs_link
[factor] |
1. no_IHS_match.0
2. IHS_match.1 |
|
 |
229
(96.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ihs_link
[factor] |
1. no_IHS_match.0
2. IHS_match.1 |
|
 |
168
(96.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ihs_link
[factor] |
1. no_IHS_match.0
2. IHS_match.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ihs_link
[factor] |
1. no_IHS_match.0
2. IHS_match.1 |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ihs_link
[factor] |
1. no_IHS_match.0
2. IHS_match.1 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RACE–NAPIIA(DERIVED API)
Description: NAPIIA is an acronym for NAACCR Asian and Pacific Islander Identification Algorithm. Race–NAPIIA(derived API) recodes some single-race cases with a Race 1 [160] code of 96 to a more specific Asian race category, based on an algorithm that makes use of the birthplace and name fields (first, last, and maiden names). For single-race cases with a Race 1 code other than 96, it returns the Race 1 code. Multiple-race cases (those with information in Race 2 through Race 5, [161-164]) are handled variously; for greater detail please refer to the technical documentation: http://www.naaccr.org/LinkClick.aspx?fileticket=3HnBhlmhkBs%3d&tabid=118&mid=458
In Version 1.1 of the algorithm, birthplace can be used to indirectly assign a specific race to one of eight Asian race groups (Chinese, Japanese, Vietnamese, Korean, Asian Indian, Filipino, Thai, and Cambodian), and names can be used to indirectly assign a specific race to one of seven Asian groups (Chinese, Japanese, Vietnamese, Korean, Asian Indian, Filipino, and Hmong). Subsequent versions of NAPIIA may incorporate Pacific Islanders and may potentially incorporate name lists for Thai, Cambodian, and Laotians.
Rationale: The use of more specific Asian and Pacific Islander codes will enhance surveillance and research activities focused on specific API subgroups.
Codes
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#193
All data
st_css() #IMPORTANT!
racenapiia <- as.factor(trimws(d[,"racenapiia"]))
levels(racenapiia)[levels(racenapiia)=="1"] <- "White.1"
levels(racenapiia)[levels(racenapiia)=="2"] <- "Black.2"
new.d <- data.frame(new.d, racenapiia)
new.d <- apply_labels(new.d, racenapiia = "race_napiia")
new.d.1 <- data.frame(new.d.1, racenapiia)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, racenapiia)
summarytools::view(dfSummary(new.d$racenapiia, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
racenapiia
[labelled, factor] |
race_napiia |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race_napiia
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race_napiia
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race_napiia
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race_napiia
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race_napiia
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race_napiia
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
race_napiia
[factor] |
1. White.1
2. Black.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SEX
All data
st_css() #IMPORTANT!
sex <- as.factor(trimws(d[,"sex"]))
levels(sex)[levels(sex)=="1"] <- "Male.1"
levels(sex)[levels(sex)=="2"] <- "Female.2"
new.d <- data.frame(new.d, sex)
new.d <- apply_labels(new.d, sex = "sex")
new.d.1 <- data.frame(new.d.1, sex)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, sex)
summarytools::view(dfSummary(new.d$sex, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sex
[labelled, factor] |
sex |
1. Male.1
2. Female.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sex
[factor] |
1. Male.1
2. Female.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sex
[factor] |
1. Male.1
2. Female.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sex
[factor] |
1. Male.1
2. Female.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sex
[factor] |
1. Male.1
2. Female.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sex
[factor] |
1. Male.1
2. Female.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sex
[factor] |
1. Male.1
2. Female.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sex
[factor] |
1. Male.1
2. Female.2 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AGE AT DIAGNOSIS
- Description: Age of the patient at diagnosis in complete years. Different tumors for the same patient may have different values.
- Codes
- 000 Less than 1 year old; diagnosed in utero
- 001 1 year old, but less than 2 years
- 002 2 years old …
- 101 101 years old …
- 120 120 years old
- 999 Unknown age
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#230
All data
st_css() #IMPORTANT!
ageatdiagnosis <- as.factor(trimws(d[,"ageatdiagnosis"]))
new.d <- data.frame(new.d, ageatdiagnosis)
new.d <- apply_labels(new.d, ageatdiagnosis = "age_at_diagnosis")
new.d.1 <- data.frame(new.d.1, ageatdiagnosis)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, ageatdiagnosis)
summarytools::view(dfSummary(new.d$ageatdiagnosis, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ageatdiagnosis
[labelled, factor] |
age_at_diagnosis |
1. 26
2. 38
3. 40
4. 41
5. 42
6. 43
7. 44
8. 45
9. 46
10. 47
11. 48
12. 49
13. 50
14. 51
15. 52
16. 53
17. 54
18. 55
19. 56
20. 57
21. 58
22. 59
23. 60
24. 61
25. 62
26. 63
27. 64
28. 65
29. 66
30. 67
31. 68
32. 69
33. 70
34. 71
35. 72
36. 73
37. 74
38. 75
39. 76
40. 77
41. 78
42. 79 |
| 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 8 | ( | 0.4% | ) | | 8 | ( | 0.4% | ) | | 16 | ( | 0.7% | ) | | 14 | ( | 0.6% | ) | | 22 | ( | 1.0% | ) | | 28 | ( | 1.3% | ) | | 36 | ( | 1.7% | ) | | 51 | ( | 2.4% | ) | | 45 | ( | 2.1% | ) | | 61 | ( | 2.8% | ) | | 79 | ( | 3.6% | ) | | 81 | ( | 3.7% | ) | | 74 | ( | 3.4% | ) | | 94 | ( | 4.3% | ) | | 122 | ( | 5.6% | ) | | 116 | ( | 5.4% | ) | | 106 | ( | 4.9% | ) | | 108 | ( | 5.0% | ) | | 104 | ( | 4.8% | ) | | 110 | ( | 5.1% | ) | | 142 | ( | 6.6% | ) | | 140 | ( | 6.5% | ) | | 117 | ( | 5.4% | ) | | 96 | ( | 4.4% | ) | | 99 | ( | 4.6% | ) | | 66 | ( | 3.0% | ) | | 54 | ( | 2.5% | ) | | 55 | ( | 2.5% | ) | | 46 | ( | 2.1% | ) | | 42 | ( | 1.9% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
age_at_diagnosis
[factor] |
1. 26
2. 38
3. 40
4. 41
5. 42
6. 43
7. 44
8. 45
9. 46
10. 47
11. 48
12. 49
13. 50
14. 51
15. 52
16. 53
17. 54
18. 55
19. 56
20. 57
21. 58
22. 59
23. 60
24. 61
25. 62
26. 63
27. 64
28. 65
29. 66
30. 67
31. 68
32. 69
33. 70
34. 71
35. 72
36. 73
37. 74
38. 75
39. 76
40. 77
41. 78
42. 79 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.1% | ) | | 2 | ( | 1.1% | ) | | 5 | ( | 2.6% | ) | | 1 | ( | 0.5% | ) | | 7 | ( | 3.7% | ) | | 9 | ( | 4.8% | ) | | 7 | ( | 3.7% | ) | | 6 | ( | 3.2% | ) | | 5 | ( | 2.6% | ) | | 17 | ( | 9.0% | ) | | 13 | ( | 6.9% | ) | | 9 | ( | 4.8% | ) | | 10 | ( | 5.3% | ) | | 8 | ( | 4.2% | ) | | 15 | ( | 7.9% | ) | | 13 | ( | 6.9% | ) | | 3 | ( | 1.6% | ) | | 7 | ( | 3.7% | ) | | 13 | ( | 6.9% | ) | | 6 | ( | 3.2% | ) | | 4 | ( | 2.1% | ) | | 5 | ( | 2.6% | ) | | 5 | ( | 2.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
age_at_diagnosis
[factor] |
1. 26
2. 38
3. 40
4. 41
5. 42
6. 43
7. 44
8. 45
9. 46
10. 47
11. 48
12. 49
13. 50
14. 51
15. 52
16. 53
17. 54
18. 55
19. 56
20. 57
21. 58
22. 59
23. 60
24. 61
25. 62
26. 63
27. 64
28. 65
29. 66
30. 67
31. 68
32. 69
33. 70
34. 71
35. 72
36. 73
37. 74
38. 75
39. 76
40. 77
41. 78
42. 79 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 3 | ( | 1.7% | ) | | 1 | ( | 0.6% | ) | | 4 | ( | 2.3% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 3 | ( | 1.7% | ) | | 4 | ( | 2.3% | ) | | 6 | ( | 3.4% | ) | | 11 | ( | 6.3% | ) | | 7 | ( | 4.0% | ) | | 10 | ( | 5.7% | ) | | 7 | ( | 4.0% | ) | | 9 | ( | 5.2% | ) | | 6 | ( | 3.4% | ) | | 7 | ( | 4.0% | ) | | 13 | ( | 7.5% | ) | | 10 | ( | 5.7% | ) | | 12 | ( | 6.9% | ) | | 10 | ( | 5.7% | ) | | 11 | ( | 6.3% | ) | | 9 | ( | 5.2% | ) | | 3 | ( | 1.7% | ) | | 3 | ( | 1.7% | ) | | 6 | ( | 3.4% | ) | | 3 | ( | 1.7% | ) | | 4 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
age_at_diagnosis
[factor] |
1. 26
2. 38
3. 40
4. 41
5. 42
6. 43
7. 44
8. 45
9. 46
10. 47
11. 48
12. 49
13. 50
14. 51
15. 52
16. 53
17. 54
18. 55
19. 56
20. 57
21. 58
22. 59
23. 60
24. 61
25. 62
26. 63
27. 64
28. 65
29. 66
30. 67
31. 68
32. 69
33. 70
34. 71
35. 72
36. 73
37. 74
38. 75
39. 76
40. 77
41. 78
42. 79 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 4 | ( | 1.7% | ) | | 6 | ( | 2.5% | ) | | 10 | ( | 4.2% | ) | | 7 | ( | 3.0% | ) | | 7 | ( | 3.0% | ) | | 13 | ( | 5.5% | ) | | 10 | ( | 4.2% | ) | | 9 | ( | 3.8% | ) | | 6 | ( | 2.5% | ) | | 18 | ( | 7.6% | ) | | 15 | ( | 6.3% | ) | | 8 | ( | 3.4% | ) | | 14 | ( | 5.9% | ) | | 9 | ( | 3.8% | ) | | 10 | ( | 4.2% | ) | | 13 | ( | 5.5% | ) | | 16 | ( | 6.8% | ) | | 8 | ( | 3.4% | ) | | 7 | ( | 3.0% | ) | | 12 | ( | 5.1% | ) | | 10 | ( | 4.2% | ) | | 7 | ( | 3.0% | ) | | 4 | ( | 1.7% | ) | | 3 | ( | 1.3% | ) | | 4 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
age_at_diagnosis
[factor] |
1. 26
2. 38
3. 40
4. 41
5. 42
6. 43
7. 44
8. 45
9. 46
10. 47
11. 48
12. 49
13. 50
14. 51
15. 52
16. 53
17. 54
18. 55
19. 56
20. 57
21. 58
22. 59
23. 60
24. 61
25. 62
26. 63
27. 64
28. 65
29. 66
30. 67
31. 68
32. 69
33. 70
34. 71
35. 72
36. 73
37. 74
38. 75
39. 76
40. 77
41. 78
42. 79 |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 3 | ( | 1.7% | ) | | 3 | ( | 1.7% | ) | | 2 | ( | 1.1% | ) | | 5 | ( | 2.9% | ) | | 3 | ( | 1.7% | ) | | 6 | ( | 3.4% | ) | | 6 | ( | 3.4% | ) | | 9 | ( | 5.2% | ) | | 4 | ( | 2.3% | ) | | 12 | ( | 6.9% | ) | | 14 | ( | 8.0% | ) | | 11 | ( | 6.3% | ) | | 12 | ( | 6.9% | ) | | 7 | ( | 4.0% | ) | | 5 | ( | 2.9% | ) | | 6 | ( | 3.4% | ) | | 4 | ( | 2.3% | ) | | 4 | ( | 2.3% | ) | | 9 | ( | 5.2% | ) | | 8 | ( | 4.6% | ) | | 4 | ( | 2.3% | ) | | 6 | ( | 3.4% | ) | | 4 | ( | 2.3% | ) | | 7 | ( | 4.0% | ) | | 2 | ( | 1.1% | ) | | 4 | ( | 2.3% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
age_at_diagnosis
[factor] |
1. 26
2. 38
3. 40
4. 41
5. 42
6. 43
7. 44
8. 45
9. 46
10. 47
11. 48
12. 49
13. 50
14. 51
15. 52
16. 53
17. 54
18. 55
19. 56
20. 57
21. 58
22. 59
23. 60
24. 61
25. 62
26. 63
27. 64
28. 65
29. 66
30. 67
31. 68
32. 69
33. 70
34. 71
35. 72
36. 73
37. 74
38. 75
39. 76
40. 77
41. 78
42. 79 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 3 | ( | 1.0% | ) | | 3 | ( | 1.0% | ) | | 2 | ( | 0.7% | ) | | 4 | ( | 1.4% | ) | | 3 | ( | 1.0% | ) | | 10 | ( | 3.4% | ) | | 7 | ( | 2.4% | ) | | 8 | ( | 2.8% | ) | | 13 | ( | 4.5% | ) | | 13 | ( | 4.5% | ) | | 5 | ( | 1.7% | ) | | 11 | ( | 3.8% | ) | | 19 | ( | 6.6% | ) | | 14 | ( | 4.8% | ) | | 15 | ( | 5.2% | ) | | 19 | ( | 6.6% | ) | | 15 | ( | 5.2% | ) | | 12 | ( | 4.1% | ) | | 25 | ( | 8.6% | ) | | 14 | ( | 4.8% | ) | | 18 | ( | 6.2% | ) | | 9 | ( | 3.1% | ) | | 11 | ( | 3.8% | ) | | 7 | ( | 2.4% | ) | | 8 | ( | 2.8% | ) | | 7 | ( | 2.4% | ) | | 4 | ( | 1.4% | ) | | 9 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
age_at_diagnosis
[factor] |
1. 26
2. 38
3. 40
4. 41
5. 42
6. 43
7. 44
8. 45
9. 46
10. 47
11. 48
12. 49
13. 50
14. 51
15. 52
16. 53
17. 54
18. 55
19. 56
20. 57
21. 58
22. 59
23. 60
24. 61
25. 62
26. 63
27. 64
28. 65
29. 66
30. 67
31. 68
32. 69
33. 70
34. 71
35. 72
36. 73
37. 74
38. 75
39. 76
40. 77
41. 78
42. 79 |
| 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 7 | ( | 0.6% | ) | | 8 | ( | 0.7% | ) | | 6 | ( | 0.6% | ) | | 16 | ( | 1.5% | ) | | 17 | ( | 1.6% | ) | | 21 | ( | 1.9% | ) | | 20 | ( | 1.8% | ) | | 35 | ( | 3.2% | ) | | 35 | ( | 3.2% | ) | | 32 | ( | 2.9% | ) | | 38 | ( | 3.5% | ) | | 50 | ( | 4.6% | ) | | 54 | ( | 5.0% | ) | | 52 | ( | 4.8% | ) | | 49 | ( | 4.5% | ) | | 52 | ( | 4.8% | ) | | 57 | ( | 5.2% | ) | | 60 | ( | 5.5% | ) | | 74 | ( | 6.8% | ) | | 81 | ( | 7.5% | ) | | 68 | ( | 6.3% | ) | | 54 | ( | 5.0% | ) | | 50 | ( | 4.6% | ) | | 34 | ( | 3.1% | ) | | 28 | ( | 2.6% | ) | | 26 | ( | 2.4% | ) | | 29 | ( | 2.7% | ) | | 19 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
age_at_diagnosis
[factor] |
1. 26
2. 38
3. 40
4. 41
5. 42
6. 43
7. 44
8. 45
9. 46
10. 47
11. 48
12. 49
13. 50
14. 51
15. 52
16. 53
17. 54
18. 55
19. 56
20. 57
21. 58
22. 59
23. 60
24. 61
25. 62
26. 63
27. 64
28. 65
29. 66
30. 67
31. 68
32. 69
33. 70
34. 71
35. 72
36. 73
37. 74
38. 75
39. 76
40. 77
41. 78
42. 79 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 2 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 12.5% | ) | | 2 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DATE OF BIRTH
Description: Date of birth of the patient. See Chapter X for date format. If age at diagnosis and year of diagnosis are known, but year of birth is unknown, then year of birth should be calculated and so coded. Only the year should be entered, left-justified. Estimate date of birth when information is not available. It is better to estimate than to leave birth date unknown.
date var
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#240
All data
st_css() #IMPORTANT!
dateofbirth <- trimws(d[,"dateofbirth"])
new.d <- data.frame(new.d, dateofbirth)
new.d <- apply_labels(new.d, dateofbirth = "date_of_birth")
#new.d.1 <- data.frame(new.d.1, dateofbirth)
#Using kable function to form a nice table
temp.d <- data.frame (new.d.1, dateofbirth)
summarytools::view(dfSummary(new.d$dateofbirth, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
dateofbirth
[labelled, character] |
date_of_birth |
1. 195012
2. 195405
3. 195411
4. 19550799
5. 194701
6. 194901
7. 194908
8. 19490904
9. 194911
10. 19500117
11. 195105
12. 195201
13. 195202
14. 195308
15. 195408
16. 195412
17. 19560199
18. 195605
19. 19570999
20. 196003
21. 196106
22. 196207
23. 194206
24. 19430499
25. 194312
26. 194508
27. 19461031
28. 194702
29. 194811
30. 19490415
31. 194905
32. 19490699
33. 19490999
34. 194910
35. 195001
36. 195002
37. 195005
38. 19500629
39. 195007
40. 195009
41. 195101
42. 195110
43. 19520899
44. 195307
45. 19530728
46. 195310
47. 19531204
48. 195406
49. 195410
50. 195509
51. 195511
52. 195603
53. 19570129
54. 195708
55. 19580999
56. 195810
57. 195901
58. 196006
59. 196007
60. 196008
61. 19600899
62. 196011
63. 196012
64. 19410519
65. 19410521
66. 194106
67. 19410806
68. 194201
69. 19420224
70. 19420330
71. 19420607
72. 194208
73. 19421201
74. 194303
75. 19431009
76. 19440531
77. 19440712
78. 194408
79. 19441212
80. 194501
81. 194504
82. 194506
83. 19450824
84. 194509
85. 194511
86. 19451128
87. 194601
88. 19460129
89. 19460227
90. 194606
91. 194607
92. 19460729
93. 194609
94. 19460910
95. 194610
96. 194611
97. 19461105
98. 19461121
99. 194612
100. 19470299
[ 1685 others ] |
| 5 | ( | 0.2% | ) | | 5 | ( | 0.2% | ) | | 5 | ( | 0.2% | ) | | 5 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1878 | ( | 86.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_birth
[character] |
1. 195201
2. 195012
3. 195310
4. 195408
5. 195411
6. 195509
7. 194201
8. 194206
9. 194303
10. 194408
11. 194511
12. 194606
13. 194610
14. 194612
15. 194702
16. 194703
17. 194805
18. 194901
19. 194908
20. 194910
21. 195002
22. 195004
23. 195005
24. 195007
25. 195101
26. 195105
27. 195203
28. 195405
29. 195412
30. 195504
31. 195603
32. 195608
33. 195609
34. 195806
35. 195807
36. 195901
37. 196003
38. 196102
39. 196106
40. 196306
41. 196510
42. 196605
43. 193909
44. 194012
45. 194204
46. 194209
47. 194308
48. 194312
49. 194401
50. 194403
[ 90 others ] |
| 4 | ( | 2.1% | ) | | 3 | ( | 1.6% | ) | | 3 | ( | 1.6% | ) | | 3 | ( | 1.6% | ) | | 3 | ( | 1.6% | ) | | 3 | ( | 1.6% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 90 | ( | 47.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_birth
[character] |
1. 19520317
2. 19580103
3. 19370416
4. 19380429
5. 19410710
6. 19411231
7. 19420224
8. 19420414
9. 19420807
10. 19420913
11. 19421122
12. 19421201
13. 19421223
14. 19421231
15. 19430525
16. 19431111
17. 19431119
18. 19440124
19. 19440223
20. 19440512
21. 19450529
22. 19450617
23. 19451025
24. 19451106
25. 19451119
26. 19460307
27. 19460506
28. 19460705
29. 19460821
30. 19460918
31. 19461014
32. 19461121
33. 19461209
34. 19470207
35. 19470321
36. 19470508
37. 19470610
38. 19470701
39. 19470914
40. 19471003
41. 19471019
42. 19471023
43. 19471208
44. 19471221
45. 19480402
46. 19480506
47. 19480615
48. 19480818
49. 19481123
50. 19481213
[ 122 others ] |
| 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 122 | ( | 70.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_birth
[character] |
1. 194701
2. 194911
3. 195202
4. 195308
5. 195405
6. 195511
7. 195605
8. 196007
9. 196011
10. 196207
11. 194106
12. 194208
13. 194312
14. 194506
15. 194508
16. 194509
17. 194607
18. 194609
19. 194706
20. 194710
21. 194802
22. 194807
23. 194811
24. 194901
25. 194905
26. 194908
27. 194909
28. 195001
29. 195003
30. 195009
31. 195012
32. 195105
33. 195106
34. 195107
35. 195108
36. 195110
37. 195303
38. 195307
39. 195402
40. 195406
41. 195410
42. 195411
43. 195412
44. 195505
45. 195606
46. 195702
47. 195705
48. 195708
49. 195711
50. 195810
[ 114 others ] |
| 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 127 | ( | 53.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_birth
[character] |
1. 19550799
2. 19570999
3. 19430499
4. 19490999
5. 19520899
6. 19560199
7. 19580999
8. 19600899
9. 19470299
10. 19480499
11. 19490699
12. 19490799
13. 19500699
14. 19540199
15. 19540699
16. 19540999
17. 19550199
18. 19550899
19. 19551299
20. 19560399
21. 19561199
22. 19570399
23. 19580299
24. 19581299
25. 19601099
26. 19610899
27. 19631099
28. 19641099
29. 19390599
30. 19390899
31. 19400699
32. 19401299
33. 19410799
34. 19410899
35. 19420899
36. 19430199
37. 19430399
38. 19430699
39. 19430799
40. 19431299
41. 19440299
42. 19440499
43. 19440699
44. 19440899
45. 19440999
46. 19450199
47. 19450299
48. 19450599
49. 19450699
50. 19451099
[ 86 others ] |
| 4 | ( | 2.3% | ) | | 4 | ( | 2.3% | ) | | 3 | ( | 1.7% | ) | | 3 | ( | 1.7% | ) | | 3 | ( | 1.7% | ) | | 3 | ( | 1.7% | ) | | 3 | ( | 1.7% | ) | | 3 | ( | 1.7% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 86 | ( | 49.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_birth
[character] |
1. 19491227
2. 19500924
3. 19510617
4. 19531002
5. 19551108
6. 19591227
7. 19401108
8. 19401209
9. 19410407
10. 19410521
11. 19410614
12. 19410704
13. 19410716
14. 19411002
15. 19411013
16. 19411212
17. 19420723
18. 19421208
19. 19430110
20. 19430124
21. 19430516
22. 19430705
23. 19430804
24. 19431103
25. 19440424
26. 19440615
27. 19440626
28. 19440719
29. 19440918
30. 19440925
31. 19441014
32. 19441228
33. 19450118
34. 19450319
35. 19450502
36. 19450630
37. 19450714
38. 19450820
39. 19450824
40. 19450921
41. 19451016
42. 19451105
43. 19451204
44. 19451226
45. 19460129
46. 19460131
47. 19460227
48. 19460309
49. 19460620
50. 19460802
[ 234 others ] |
| 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 234 | ( | 80.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_birth
[character] |
1. 19461031
2. 19490904
3. 19500117
4. 19410519
5. 19410806
6. 19420330
7. 19420607
8. 19431009
9. 19440531
10. 19440712
11. 19441212
12. 19451128
13. 19460729
14. 19460910
15. 19461105
16. 19470613
17. 19480104
18. 19480310
19. 19480403
20. 19480519
21. 19480709
22. 19480812
23. 19480915
24. 19481230
25. 19490221
26. 19490415
27. 19490504
28. 19490623
29. 19490802
30. 19490805
31. 19500102
32. 19500308
33. 19500607
34. 19500629
35. 19501218
36. 19510212
37. 19510303
38. 19510408
39. 19510508
40. 19510813
41. 19520118
42. 19520225
43. 19520314
44. 19520716
45. 19521012
46. 19530821
47. 19531204
48. 19540208
49. 19540510
50. 19540531
[ 965 others ] |
| 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 984 | ( | 90.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_birth
[character] |
1. 19450499
2. 19480999
3. 19490299
4. 19490699
5. 19501299
6. 19520499
7. 19530899
8. 19540299
9. 19550799
10. 19560199
11. 19561099
12. 19561299
13. 19591099
14. 19600599
15. 19620499
16. 19660799 |
| 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
BIRTHPLACE
Description: Code for place of birth of the patient. If a patient has multiple tumors, all records should contain the same code.
Rationale: Place of Birth is helpful for patient matching and can be used when reviewing race and ethnicity. In addition, adding birthplace data to race and ethnicity allows for a more specific definition of the population being reported. Careful descriptions of ancestry, birthplace, and immigration history of populations studied are needed to make the basis for classification into ethnic groups clear. Birthplace has been associated with variation in genetic, socioeconomic, cultural, and nutritional characteristics that affect patterns of disease. A better understanding of the differences within racial and ethnic categories also can help states develop effective, culturally sensitive public health prevention programs to decrease the prevalence of high-risk behaviors and increase the use of preventive services.
Note: For cases diagnosed January 1, 2013, and later, Birthplace–State [252] and Birthplace–Country [254] replace Birthplace [250].
Codes: See Appendix B for numeric and alphabetic lists of places and codes (also see Appendix B of the SEER Program Code Manual at seer.cancer.gov/tools/codingmanuals/index.html).
Many numeric codes need to be identified!
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#250
All data
st_css() #IMPORTANT!
birthplace <- as.factor(trimws(d[,"birthplace"]))
# recode for interpretable birthplace
#new.d.n <- data.frame(new.d.n, birthplace) # keep NAACCR coding
levels(birthplace)[levels(birthplace)=="999"] <- "Unkown.999"
levels(birthplace)[levels(birthplace)=="97"] <- "Sonoma (Greater California).97"
levels(birthplace)[levels(birthplace)=="75"] <- "Kiowa.75"
levels(birthplace)[levels(birthplace)=="73"] <- "Ouachita.73"
levels(birthplace)[levels(birthplace)=="5"] <- "Bristol.5"
levels(birthplace)[levels(birthplace)=="33"] <- "Burke.33"
levels(birthplace)[levels(birthplace)=="11"] <- "Cayuga.11"
levels(birthplace)[levels(birthplace)=="1"] <- "Appling.1"
new.d <- data.frame(new.d, birthplace)
new.d <- apply_labels(new.d, birthplace = "Place of birth")
temp.d <- data.frame (new.d.1, birthplace)
summarytools::view(dfSummary(new.d$birthplace, style = 'grid',
max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace
[labelled, factor] |
Place of birth |
1. 0
2. 100
3. Cayuga.11
4. 244
5. 25
6. Burke.33
7. 35
8. 37
9. 41
10. 43
11. Ouachita.73
12. 87
13. Sonoma (Greater Californi
14. Unkown.999 |
| 96 | ( | 15.8% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.3% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 44 | ( | 7.3% | ) | | 5 | ( | 0.8% | ) | | 3 | ( | 0.5% | ) | | 3 | ( | 0.5% | ) | | 1 | ( | 0.2% | ) | | 17 | ( | 2.8% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 430 | ( | 71.0% | ) |
|
 |
1561
(72.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. 0
2. 100
3. Cayuga.11
4. 244
5. 25
6. Burke.33
7. 35
8. 37
9. 41
10. 43
11. Ouachita.73
12. 87
13. Sonoma (Greater California).97
14. Unkown.999 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. 0
2. 100
3. Cayuga.11
4. 244
5. 25
6. Burke.33
7. 35
8. 37
9. 41
10. 43
11. Ouachita.73
12. 87
13. Sonoma (Greater Californi
14. Unkown.999 |
| 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 164 | ( | 97.6% | ) |
|
 |
6
(3.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. 0
2. 100
3. Cayuga.11
4. 244
5. 25
6. Burke.33
7. 35
8. 37
9. 41
10. 43
11. Ouachita.73
12. 87
13. Sonoma (Greater California).97
14. Unkown.999 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. 0
2. 100
3. Cayuga.11
4. 244
5. 25
6. Burke.33
7. 35
8. 37
9. 41
10. 43
11. Ouachita.73
12. 87
13. Sonoma (Greater Californi
14. Unkown.999 |
| 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 43 | ( | 95.6% | ) |
|
 |
129
(74.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. 0
2. 100
3. Cayuga.11
4. 244
5. 25
6. Burke.33
7. 35
8. 37
9. 41
10. 43
11. Ouachita.73
12. 87
13. Sonoma (Greater Californi
14. Unkown.999 |
| 8 | ( | 6.5% | ) | | 1 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.8% | ) | | 16 | ( | 12.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 97 | ( | 78.2% | ) |
|
 |
166
(57.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. 0
2. 100
3. Cayuga.11
4. 244
5. 25
6. Burke.33
7. 35
8. 37
9. 41
10. 43
11. Ouachita.73
12. 87
13. Sonoma (Greater Californi
14. Unkown.999 |
| 85 | ( | 33.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 44 | ( | 17.3% | ) | | 4 | ( | 1.6% | ) | | 2 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 114 | ( | 44.9% | ) |
|
 |
833
(76.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. 0
2. 100
3. Cayuga.11
4. 244
5. 25
6. Burke.33
7. 35
8. 37
9. 41
10. 43
11. Ouachita.73
12. 87
13. Sonoma (Greater Californi
14. Unkown.999 |
| 1 | ( | 6.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 13.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 80.0% | ) |
|
 |
1
(6.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
BIRTHPLACE–STATE
USPS abbreviation for the state, commonwealth, U.S. possession; or CanadaPost abbreviation for the Canadian province/territory in which the patient was born. If the patient has multiple primaries, the state of birth is the same for each tumor. This data item became part of the NAACCR transmission record effective with Volume II, Version 13 in order to include country and state for each geographic item and to use interoperable codes. It supplements the item BIRTHPLACE–COUNTRY [254]. These two data items are intended to replace the item BIRTHPLACE [250].
Rationale: This is a modification of the current item Birthplace [250] item in order to make use of standard codes, rather than using geographic codes that are only used by cancer registries. The intention is that item 250 be converted to populate the new corresponding, more standard, data items. Codes
See Appendix B for numeric and alphabetic lists of places and codes (also see Appendix B of the SEER Program Code Manual at seer.cancer.gov/tools/codingmanuals/index.html).
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#252
All data
st_css() #IMPORTANT!
birthplacestate <- as.factor(trimws(d[,"birthplacestate"]))
# recode for interpretable birthplace state
#new.d.n <- data.frame(new.d.n, birthplacestate) # keep NAACCR coding
levels(birthplacestate)[levels(birthplacestate)=="XX"] <- "Unknown.XX"
levels(birthplacestate)[levels(birthplacestate)=="YY"] <- "Unknown.YY"
levels(birthplacestate)[levels(birthplacestate)=="ZZ"] <- "Unknown.ZZ"
new.d <- data.frame(new.d, birthplacestate)
new.d <- apply_labels(new.d, birthplacestate = "State of birth")
temp.d <- data.frame (new.d.1, birthplacestate)
summarytools::view(dfSummary(new.d$birthplacestate, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplacestate
[labelled, factor] |
State of birth |
1. AK
2. AL
3. AR
4. AZ
5. CA
6. CD
7. CT
8. DE
9. FL
10. GA
11. GU
12. IA
13. IL
14. IN
15. KS
16. KY
17. LA
18. MA
19. MI
20. MO
21. MS
22. NC
23. NJ
24. NY
25. OH
26. OK
27. PA
28. SC
29. TN
30. TX
31. US
32. UT
33. VA
34. WA
35. Unknown.XX
36. Unknown.YY
37. Unknown.ZZ |
| 3 | ( | 0.1% | ) | | 17 | ( | 0.8% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 77 | ( | 3.6% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 14 | ( | 0.6% | ) | | 223 | ( | 10.3% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) | | 5 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 80 | ( | 3.7% | ) | | 1 | ( | 0.0% | ) | | 18 | ( | 0.8% | ) | | 3 | ( | 0.1% | ) | | 10 | ( | 0.5% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 9 | ( | 0.4% | ) | | 5 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 10 | ( | 0.5% | ) | | 2 | ( | 0.1% | ) | | 10 | ( | 0.5% | ) | | 383 | ( | 17.7% | ) | | 1 | ( | 0.0% | ) | | 6 | ( | 0.3% | ) | | 3 | ( | 0.1% | ) | | 38 | ( | 1.8% | ) | | 3 | ( | 0.1% | ) | | 1213 | ( | 56.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. AK
2. AL
3. AR
4. AZ
5. CA
6. CD
7. CT
8. DE
9. FL
10. GA
11. GU
12. IA
13. IL
14. IN
15. KS
16. KY
17. LA
18. MA
19. MI
20. MO
21. MS
22. NC
23. NJ
24. NY
25. OH
26. OK
27. PA
28. SC
29. TN
30. TX
31. US
32. UT
33. VA
34. WA
35. Unknown.XX
36. Unknown.YY
37. Unknown.ZZ |
| 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 27 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 3 | ( | 1.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 5 | ( | 2.6% | ) | | 12 | ( | 6.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.6% | ) | | 8 | ( | 4.2% | ) | | 2 | ( | 1.1% | ) | | 101 | ( | 53.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. AK
2. AL
3. AR
4. AZ
5. CA
6. CD
7. CT
8. DE
9. FL
10. GA
11. GU
12. IA
13. IL
14. IN
15. KS
16. KY
17. LA
18. MA
19. MI
20. MO
21. MS
22. NC
23. NJ
24. NY
25. OH
26. OK
27. PA
28. SC
29. TN
30. TX
31. US
32. UT
33. VA
34. WA
35. Unknown.XX
36. Unknown.YY
37. Unknown.ZZ |
| 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 14 | ( | 8.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 22 | ( | 12.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 114 | ( | 65.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. AK
2. AL
3. AR
4. AZ
5. CA
6. CD
7. CT
8. DE
9. FL
10. GA
11. GU
12. IA
13. IL
14. IN
15. KS
16. KY
17. LA
18. MA
19. MI
20. MO
21. MS
22. NC
23. NJ
24. NY
25. OH
26. OK
27. PA
28. SC
29. TN
30. TX
31. US
32. UT
33. VA
34. WA
35. Unknown.XX
36. Unknown.YY
37. Unknown.ZZ |
| 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 33 | ( | 13.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 6 | ( | 2.5% | ) | | 1 | ( | 0.4% | ) | | 5 | ( | 2.1% | ) | | 1 | ( | 0.4% | ) | | 3 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 4 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) | | 8 | ( | 3.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 16 | ( | 6.8% | ) | | 0 | ( | 0.0% | ) | | 143 | ( | 60.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. AK
2. AL
3. AR
4. AZ
5. CA
6. CD
7. CT
8. DE
9. FL
10. GA
11. GU
12. IA
13. IL
14. IN
15. KS
16. KY
17. LA
18. MA
19. MI
20. MO
21. MS
22. NC
23. NJ
24. NY
25. OH
26. OK
27. PA
28. SC
29. TN
30. TX
31. US
32. UT
33. VA
34. WA
35. Unknown.XX
36. Unknown.YY
37. Unknown.ZZ |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 6.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 155 | ( | 89.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. AK
2. AL
3. AR
4. AZ
5. CA
6. CD
7. CT
8. DE
9. FL
10. GA
11. GU
12. IA
13. IL
14. IN
15. KS
16. KY
17. LA
18. MA
19. MI
20. MO
21. MS
22. NC
23. NJ
24. NY
25. OH
26. OK
27. PA
28. SC
29. TN
30. TX
31. US
32. UT
33. VA
34. WA
35. Unknown.XX
36. Unknown.YY
37. Unknown.ZZ |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 68 | ( | 23.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 20 | ( | 6.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 196 | ( | 67.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. AK
2. AL
3. AR
4. AZ
5. CA
6. CD
7. CT
8. DE
9. FL
10. GA
11. GU
12. IA
13. IL
14. IN
15. KS
16. KY
17. LA
18. MA
19. MI
20. MO
21. MS
22. NC
23. NJ
24. NY
25. OH
26. OK
27. PA
28. SC
29. TN
30. TX
31. US
32. UT
33. VA
34. WA
35. Unknown.XX
36. Unknown.YY
37. Unknown.ZZ |
| 0 | ( | 0.0% | ) | | 10 | ( | 0.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 0.9% | ) | | 221 | ( | 20.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 309 | ( | 28.4% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 0.7% | ) | | 1 | ( | 0.1% | ) | | 491 | ( | 45.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_state
[factor] |
1. AK
2. AL
3. AR
4. AZ
5. CA
6. CD
7. CT
8. DE
9. FL
10. GA
11. GU
12. IA
13. IL
14. IN
15. KS
16. KY
17. LA
18. MA
19. MI
20. MO
21. MS
22. NC
23. NJ
24. NY
25. OH
26. OK
27. PA
28. SC
29. TN
30. TX
31. US
32. UT
33. VA
34. WA
35. Unknown.XX
36. Unknown.YY
37. Unknown.ZZ |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 13 | ( | 81.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
BIRTHPLACE–COUNTRY
Code for the country in which the patient was born. If the patient has multiple tumors, all records should contain the same code. This data item became part of the NAACCR transmission record effective with Volume II, Version 13 in order to include country and state for each geographic item and to use interoperable codes. It supplements the item BIRTHPLACE–STATE [252]. These two data items are intended to replace the use of BIRTHPLACE [250].
Rationale: Place of Birth is helpful for patient matching and can be used when reviewing race and ethnicity. It is an important item in algorithms for imputing race and ethnicity. In addition, adding birthplace data to race and ethnicity allows for a more specific definition of the population being reported. Careful descriptions of ancestry, birthplace, and immigration history of populations studied are needed to make the basis for classification into ethnic groups clear. Birthplace has been associated with variation in genetic, socioeconomic, cultural, and nutritional characteristics that affect patterns of disease. A better understanding of the differences within racial and ethnic categories also can help states develop effective, culturally-sensitive public health prevention programs to decrease the prevalence of high-risk behaviors and increase the use of preventive services.
See Appendix B for numeric and alphabetic lists of places and codes (also see Appendix B of the SEER Program Code Manual at seer.cancer.gov/tools/codingmanuals/index.html).
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#254
All data
st_css() #IMPORTANT!}
birthplacecountry <- as.factor(trimws(d[,"birthplacecountry"]))
#new.d.n <- data.frame(new.d.n, birthplacecountry) # keep NAACCR coding
# recode for interpretable birthplace country
levels(birthplacecountry)[levels(birthplacecountry)=="ZZU"] <- "Unknown.ZZU"
levels(birthplacecountry)[levels(birthplacecountry)=="ZZF"] <- "Africa.ZZF"
levels(birthplacecountry)[levels(birthplacecountry)=="ZZX"] <- "Non_US.ZZX"
levels(birthplacecountry)[levels(birthplacecountry)=="ZZE"] <- "Europe NOS.ZZE"
levels(birthplacecountry)[levels(birthplacecountry)=="ZZC"] <- "Central American NOS.ZZC"
levels(birthplacecountry)[levels(birthplacecountry)=="ZWE"] <- "Zimbabwe.ZWE"
levels(birthplacecountry)[levels(birthplacecountry)=="XWF"] <- "West Africa, NOS (French Africa, NOS).XWF"
levels(birthplacecountry)[levels(birthplacecountry)=="WSM"] <- "Samoa.WSM"
levels(birthplacecountry)[levels(birthplacecountry)=="VIR"] <- "Virgin Islands (U.S.).VIR"
levels(birthplacecountry)[levels(birthplacecountry)=="USA"] <- "United States of America (the).USA"
levels(birthplacecountry)[levels(birthplacecountry)=="UGA"] <- "Uganda.UGA"
levels(birthplacecountry)[levels(birthplacecountry)=="TTO"] <- "Trinidad and Tobago.TTO"
levels(birthplacecountry)[levels(birthplacecountry)=="SOM"] <- "Somalia.SOM"
levels(birthplacecountry)[levels(birthplacecountry)=="SLE"] <- "Sierra Leone.SLE"
levels(birthplacecountry)[levels(birthplacecountry)=="SEN"] <- "Senegal.SEN"
levels(birthplacecountry)[levels(birthplacecountry)=="RWA"] <- "Rwanda.RWA"
levels(birthplacecountry)[levels(birthplacecountry)=="PRT"] <- "Portugal.PRT"
levels(birthplacecountry)[levels(birthplacecountry)=="PRI"] <- "Puerto Rico.PRI"
levels(birthplacecountry)[levels(birthplacecountry)=="PAN"] <- "Panama.PAN"
levels(birthplacecountry)[levels(birthplacecountry)=="NIC"] <- "Nicaragua.NIC"
levels(birthplacecountry)[levels(birthplacecountry)=="NGA"] <- "Nigeria.NGA"
levels(birthplacecountry)[levels(birthplacecountry)=="NER"] <- "Niger.NER"
levels(birthplacecountry)[levels(birthplacecountry)=="MEX"] <- "Mexico.MEX"
levels(birthplacecountry)[levels(birthplacecountry)=="LCA"] <- "Saint Lucia.LCA"
levels(birthplacecountry)[levels(birthplacecountry)=="LBR"] <- "Liberia.LBR"
levels(birthplacecountry)[levels(birthplacecountry)=="JPN"] <- "Japan.JPN"
levels(birthplacecountry)[levels(birthplacecountry)=="JAM"] <- "Jamaica.JAM"
levels(birthplacecountry)[levels(birthplacecountry)=="HUN"] <- "Hungary.HUN"
levels(birthplacecountry)[levels(birthplacecountry)=="HTI"] <- "Haiti.HTI"
levels(birthplacecountry)[levels(birthplacecountry)=="GUY"] <- "Guyana.GUY"
levels(birthplacecountry)[levels(birthplacecountry)=="GUM"] <- "Guam.GUM"
levels(birthplacecountry)[levels(birthplacecountry)=="GRD"] <- "Grenada.GRD"
levels(birthplacecountry)[levels(birthplacecountry)=="GMB"] <- "Gambia (the).GMB"
levels(birthplacecountry)[levels(birthplacecountry)=="GHA"] <- "Ghana.GHA"
levels(birthplacecountry)[levels(birthplacecountry)=="GEO"] <- "Georgia.GEO"
levels(birthplacecountry)[levels(birthplacecountry)=="GBR"] <- "United Kingdom of Great Britain and Northern Ireland (the).GBR"
levels(birthplacecountry)[levels(birthplacecountry)=="FRA"] <- "France.FRA"
levels(birthplacecountry)[levels(birthplacecountry)=="ETH"] <- "Ethiopia.ETH"
levels(birthplacecountry)[levels(birthplacecountry)=="ERI"] <- "Eritrea.ERI"
levels(birthplacecountry)[levels(birthplacecountry)=="ENG"] <- "England.ENG"
levels(birthplacecountry)[levels(birthplacecountry)=="DOM"] <- "Dominican Republic (the).DOM"
levels(birthplacecountry)[levels(birthplacecountry)=="DEU"] <- "Germany.DEU"
levels(birthplacecountry)[levels(birthplacecountry)=="CUB"] <- "Cuba.CUB"
levels(birthplacecountry)[levels(birthplacecountry)=="CRI"] <- "Costa Rica.CRI"
levels(birthplacecountry)[levels(birthplacecountry)=="COG"] <- "Congo (the).COG"
levels(birthplacecountry)[levels(birthplacecountry)=="CMR"] <- "Cameroon.CMR"
levels(birthplacecountry)[levels(birthplacecountry)=="CIV"] <- "Cote d'Ivoire.CIV"
levels(birthplacecountry)[levels(birthplacecountry)=="CAN"] <- "Canada.CAN"
levels(birthplacecountry)[levels(birthplacecountry)=="BRB"] <- "Barbados.BRB"
levels(birthplacecountry)[levels(birthplacecountry)=="BLZ"] <- "Belize.BLZ"
levels(birthplacecountry)[levels(birthplacecountry)=="BHS"] <- "Bahamas (the).BHS"
levels(birthplacecountry)[levels(birthplacecountry)=="ARE"] <- "United Arab Emirates (the).ARE"
new.d <- data.frame(new.d, birthplacecountry)
new.d <- apply_labels(new.d, birthplacecountry = "Country of birth")
temp.d <- data.frame (new.d.1, birthplacecountry)
summarytools::view(dfSummary(new.d$birthplacecountry, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplacecountry
[labelled, factor] |
Country of birth |
1. Bahamas (the).BHS
2. Belize.BLZ
3. Canada.CAN
4. Cameroon.CMR
5. Cuba.CUB
6. England.ENG
7. Ethiopia.ETH
8. France.FRA
9. Georgia.GEO
10. Ghana.GHA
11. Gambia (the).GMB
12. Guam.GUM
13. Haiti.HTI
14. Jamaica.JAM
15. KEN
16. Niger.NER
17. Nigeria.NGA
18. Nicaragua.NIC
19. Panama.PAN
20. Senegal.SEN
21. Trinidad and Tobago.TTO
22. United States of America
23. Africa.ZZF
24. Unknown.ZZU
25. Non_US.ZZX |
| 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 9 | ( | 0.4% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 10 | ( | 0.5% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 911 | ( | 42.0% | ) | | 1 | ( | 0.0% | ) | | 1214 | ( | 56.0% | ) | | 1 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_country
[factor] |
1. Bahamas (the).BHS
2. Belize.BLZ
3. Canada.CAN
4. Cameroon.CMR
5. Cuba.CUB
6. England.ENG
7. Ethiopia.ETH
8. France.FRA
9. Georgia.GEO
10. Ghana.GHA
11. Gambia (the).GMB
12. Guam.GUM
13. Haiti.HTI
14. Jamaica.JAM
15. KEN
16. Niger.NER
17. Nigeria.NGA
18. Nicaragua.NIC
19. Panama.PAN
20. Senegal.SEN
21. Trinidad and Tobago.TTO
22. United States of America
23. Africa.ZZF
24. Unknown.ZZU
25. Non_US.ZZX |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 77 | ( | 40.7% | ) | | 1 | ( | 0.5% | ) | | 102 | ( | 54.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_country
[factor] |
1. Bahamas (the).BHS
2. Belize.BLZ
3. Canada.CAN
4. Cameroon.CMR
5. Cuba.CUB
6. England.ENG
7. Ethiopia.ETH
8. France.FRA
9. Georgia.GEO
10. Ghana.GHA
11. Gambia (the).GMB
12. Guam.GUM
13. Haiti.HTI
14. Jamaica.JAM
15. KEN
16. Niger.NER
17. Nigeria.NGA
18. Nicaragua.NIC
19. Panama.PAN
20. Senegal.SEN
21. Trinidad and Tobago.TTO
22. United States of America
23. Africa.ZZF
24. Unknown.ZZU
25. Non_US.ZZX |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 54 | ( | 31.0% | ) | | 0 | ( | 0.0% | ) | | 114 | ( | 65.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_country
[factor] |
1. Bahamas (the).BHS
2. Belize.BLZ
3. Canada.CAN
4. Cameroon.CMR
5. Cuba.CUB
6. England.ENG
7. Ethiopia.ETH
8. France.FRA
9. Georgia.GEO
10. Ghana.GHA
11. Gambia (the).GMB
12. Guam.GUM
13. Haiti.HTI
14. Jamaica.JAM
15. KEN
16. Niger.NER
17. Nigeria.NGA
18. Nicaragua.NIC
19. Panama.PAN
20. Senegal.SEN
21. Trinidad and Tobago.TTO
22. United States of America
23. Africa.ZZF
24. Unknown.ZZU
25. Non_US.ZZX |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 78 | ( | 32.9% | ) | | 0 | ( | 0.0% | ) | | 143 | ( | 60.3% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_country
[factor] |
1. Bahamas (the).BHS
2. Belize.BLZ
3. Canada.CAN
4. Cameroon.CMR
5. Cuba.CUB
6. England.ENG
7. Ethiopia.ETH
8. France.FRA
9. Georgia.GEO
10. Ghana.GHA
11. Gambia (the).GMB
12. Guam.GUM
13. Haiti.HTI
14. Jamaica.JAM
15. KEN
16. Niger.NER
17. Nigeria.NGA
18. Nicaragua.NIC
19. Panama.PAN
20. Senegal.SEN
21. Trinidad and Tobago.TTO
22. United States of America
23. Africa.ZZF
24. Unknown.ZZU
25. Non_US.ZZX |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 19 | ( | 10.9% | ) | | 0 | ( | 0.0% | ) | | 155 | ( | 89.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_country
[factor] |
1. Bahamas (the).BHS
2. Belize.BLZ
3. Canada.CAN
4. Cameroon.CMR
5. Cuba.CUB
6. England.ENG
7. Ethiopia.ETH
8. France.FRA
9. Georgia.GEO
10. Ghana.GHA
11. Gambia (the).GMB
12. Guam.GUM
13. Haiti.HTI
14. Jamaica.JAM
15. KEN
16. Niger.NER
17. Nigeria.NGA
18. Nicaragua.NIC
19. Panama.PAN
20. Senegal.SEN
21. Trinidad and Tobago.TTO
22. United States of America
23. Africa.ZZF
24. Unknown.ZZU
25. Non_US.ZZX |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 93 | ( | 32.1% | ) | | 0 | ( | 0.0% | ) | | 196 | ( | 67.6% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_country
[factor] |
1. Bahamas (the).BHS
2. Belize.BLZ
3. Canada.CAN
4. Cameroon.CMR
5. Cuba.CUB
6. England.ENG
7. Ethiopia.ETH
8. France.FRA
9. Georgia.GEO
10. Ghana.GHA
11. Gambia (the).GMB
12. Guam.GUM
13. Haiti.HTI
14. Jamaica.JAM
15. KEN
16. Niger.NER
17. Nigeria.NGA
18. Nicaragua.NIC
19. Panama.PAN
20. Senegal.SEN
21. Trinidad and Tobago.TTO
22. United States of America
23. Africa.ZZF
24. Unknown.ZZU
25. Non_US.ZZX |
| 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 587 | ( | 54.0% | ) | | 0 | ( | 0.0% | ) | | 491 | ( | 45.2% | ) | | 1 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
birthplace_country
[factor] |
1. Bahamas (the).BHS
2. Belize.BLZ
3. Canada.CAN
4. Cameroon.CMR
5. Cuba.CUB
6. England.ENG
7. Ethiopia.ETH
8. France.FRA
9. Georgia.GEO
10. Ghana.GHA
11. Gambia (the).GMB
12. Guam.GUM
13. Haiti.HTI
14. Jamaica.JAM
15. KEN
16. Niger.NER
17. Nigeria.NGA
18. Nicaragua.NIC
19. Panama.PAN
20. Senegal.SEN
21. Trinidad and Tobago.TTO
22. United States of America
23. Africa.ZZF
24. Unknown.ZZU
25. Non_US.ZZX |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) | | 0 | ( | 0.0% | ) | | 13 | ( | 81.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CENSUS BLOCK GROUP 2000
- Description: This field is provided for coding the block group of patient’s residence at time of diagnosis, as defined by the 2000 Census.
- Rationale: A block group is a subdivision of a census tract designed to have an average of 1500 people, versus a census tract’s average of 4500 people. All land area in the United States is described by a census block group in the 2000 Census. The Census Bureau publishes detailed population and socioeconomic data at this level.
- Block groups thus offer a high level of specificity for geographical and socioeconomic analyses. A block group has no meaning in the absence of a census tract. Refer to Census Tr Certainty 2000 [365] to ascertain basis of assignment of Census Block Group 2000.
- Comment: Numerous registries find the distinction between “attempted, could not be determined” (zero) and “not coded” (blank) to be useful for geocoding planning purposes.
- Note: The values 1 through 9 are nominal, with no hierarchy of values. This number determines the first digit of all the blocks which comprise the block group; for instance, census block group 3 would contain blocks numbered 3000 to 3999.
- Codes
- 0 Census block group assignment was attempted, but the value could not be determined
- 1-9 Census block group values as defined by the Census Bureau
- Blank Census Block Group 2000 not coded
- Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#362
All data
st_css() #IMPORTANT!
censusblockgroup2000 <- as.factor(trimws(d[,"censusblockgroup2000"]))
new.d <- data.frame(new.d, censusblockgroup2000)
new.d <- apply_labels(new.d, censusblockgroup2000 = "census_block_group_2000")
temp.d <- data.frame (new.d.1, censusblockgroup2000)
summarytools::view(dfSummary(new.d$censusblockgroup2000, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
censusblockgroup2000
[labelled, factor] |
census_block_group_2000 |
1. 1 |
|
 |
2166
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2000
[factor] |
1. 1 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2000
[factor] |
1. 1 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2000
[factor] |
1. 1 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2000
[factor] |
1. 1 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2000
[factor] |
1. 1 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2000
[factor] |
1. 1 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2000
[factor] |
1. 1 |
|
 |
15
(93.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CENSUS BLOCK GROUP 2010
Description: This field is provided for coding the block group of patient’s residence at time of diagnosis, as defined by the 2010 Census.
Rationale: A block group is a subdivision of a census tract designed to have an average of 1500 people, versus a census tract’s average of 4500 people. All land area in the United States is described by a census block group in the 2010 Census. The Census Bureau publishes detailed population and socioeconomic data at this level. Block groups thus offer a high level of specificity for geographical and socioeconomic analyses.
A block group has no meaning in the absence of a census tract. Refer to Census Tr Certainty 2010 [367] to ascertain basis of assignment of Census Block Group 2010.
Comment: Numerous registries find the distinction between “attempted, could not be determined” (zero) and “not coded” (blank) to be useful for geocoding planning purposes.
Note: The values 1 through 9 are nominal, with no hierarchy of values. This number determines the first digit of all the blocks which comprise the block group; for instance, census block group 3 would contain blocks numbered 3000 to 3999.
Codes
- 0 Census block group assignment was attempted, but the value could not be determined
- 1-9 Census block group values as defined by the Census Bureau
- Blank Census Block Group 2010 not coded
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#363
All data
st_css() #IMPORTANT!
censusblockgroup2010 <- as.factor(trimws(d[,"censusblockgroup2010"]))
new.d <- data.frame(new.d, censusblockgroup2010)
new.d <- apply_labels(new.d, censusblockgroup2010 = "census_block_group_2010")
temp.d <- data.frame (new.d.1, censusblockgroup2010)
summarytools::view(dfSummary(new.d$censusblockgroup2010, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
censusblockgroup2010
[labelled, factor] |
census_block_group_2010 |
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9 |
| 672 | ( | 34.8% | ) | | 527 | ( | 27.3% | ) | | 364 | ( | 18.9% | ) | | 240 | ( | 12.4% | ) | | 62 | ( | 3.2% | ) | | 7 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 55 | ( | 2.9% | ) |
|
 |
238
(11.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2010
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9 |
| 61 | ( | 32.4% | ) | | 63 | ( | 33.5% | ) | | 40 | ( | 21.3% | ) | | 17 | ( | 9.0% | ) | | 4 | ( | 2.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1
(0.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2010
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9 |
| 67 | ( | 38.5% | ) | | 52 | ( | 29.9% | ) | | 37 | ( | 21.3% | ) | | 17 | ( | 9.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2010
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2010
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9 |
| 56 | ( | 32.2% | ) | | 56 | ( | 32.2% | ) | | 37 | ( | 21.3% | ) | | 17 | ( | 9.8% | ) | | 8 | ( | 4.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2010
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9 |
| 91 | ( | 31.4% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 3.4% | ) | | 111 | ( | 38.3% | ) | | 22 | ( | 7.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 55 | ( | 19.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2010
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9 |
| 390 | ( | 35.9% | ) | | 351 | ( | 32.3% | ) | | 237 | ( | 21.8% | ) | | 78 | ( | 7.2% | ) | | 27 | ( | 2.5% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_block_group_2010
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 5
6. 6
7. 7
8. 8
9. 9 |
| 7 | ( | 43.8% | ) | | 5 | ( | 31.2% | ) | | 3 | ( | 18.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CENSUS TR CERTAINTY 2000
- Description: Code indicating basis of assignment of census tract for an individual record. Helpful in identifying cases tracted from incomplete information or P.O. Box. This item is not coded by the hospital. Central registry staff assign the code.
- Codes
- 1 Census tract based on complete and valid street address of residence
- 2 Census tract based on residence ZIP + 4
- 3 Census tract based on residence ZIP + 2
- 4 Census tract based on residence ZIP code only
- 5 Census tract based on ZIP code of P.O. Box
- 6 Census tract/BNA based on residence city where city has only one census tract, or based on residence ZIP code where ZIP code has only one census tract
- 9 Not assigned, geocoding attempted
- Blank Not assigned, geocoding not attempted
- Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#365
All data
st_css() #IMPORTANT!
censustrcertainty2000 <- as.factor(trimws(d[,"censustrcertainty2000"]))
levels(censustrcertainty2000)[levels(censustrcertainty2000)=="1"] <- "complete_and_valid.1"
levels(censustrcertainty2000)[levels(censustrcertainty2000)=="9"] <- "Not_assigned.9"
new.d <- data.frame(new.d, censustrcertainty2000)
new.d <- apply_labels(new.d, censustrcertainty2000 = "census_tr_certainty_2000")
temp.d <- data.frame (new.d.1, censustrcertainty2000)
summarytools::view(dfSummary(new.d$censustrcertainty2000, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
censustrcertainty2000
[labelled, factor] |
census_tr_certainty_2000 |
1. complete_and_valid.1
2. Not_assigned.9 |
|
 |
1559
(71.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2000
[factor] |
1. complete_and_valid.1
2. Not_assigned.9 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2000
[factor] |
1. complete_and_valid.1
2. Not_assigned.9 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2000
[factor] |
1. complete_and_valid.1
2. Not_assigned.9 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2000
[factor] |
1. complete_and_valid.1
2. Not_assigned.9 |
|
 |
173
(99.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2000
[factor] |
1. complete_and_valid.1
2. Not_assigned.9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2000
[factor] |
1. complete_and_valid.1
2. Not_assigned.9 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2000
[factor] |
1. complete_and_valid.1
2. Not_assigned.9 |
|
 |
9
(56.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CENSUS TR CERTAINTY 2000
Description: Code indicating basis of assignment of census tract for an individual record. Helpful in identifying cases tracted from incomplete information or P.O. Box. This item is not coded by the hospital. Central registry staff assign the code.
Codes
- 1 Census tract based on complete and valid street address of residence
- 2 Census tract based on residence ZIP + 4
- 3 Census tract based on residence ZIP + 2
- 4 Census tract based on residence ZIP code only
- 5 Census tract based on ZIP code of P.O. Box
- 6 Census tract/BNA based on residence city where city has only one census tract, or based on residence ZIP code where ZIP code has only one census tract
- 9 Not assigned, geocoding attempted
- Blank Not assigned, geocoding not attempted
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#367
All data
st_css() #IMPORTANT!
censustrcertainty2010 <- as.factor(trimws(d[,"censustrcertainty2010"]))
levels(censustrcertainty2010)[levels(censustrcertainty2010)=="1"] <- "complete_and_valid.1"
levels(censustrcertainty2010)[levels(censustrcertainty2010)=="2"] <- "ZIP_4.2"
levels(censustrcertainty2010)[levels(censustrcertainty2010)=="4"] <- "ZIP_code_only.4"
levels(censustrcertainty2010)[levels(censustrcertainty2010)=="5"] <- "ZIP_code_of_PO_Box.5"
levels(censustrcertainty2010)[levels(censustrcertainty2010)=="9"] <- "Not_assigned.9"
new.d <- data.frame(new.d, censustrcertainty2010)
new.d <- apply_labels(new.d, censustrcertainty2010 = "census_tr_certainty_2010")
temp.d <- data.frame (new.d.1, censustrcertainty2010)
summarytools::view(dfSummary(new.d$censustrcertainty2010, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
censustrcertainty2010
[labelled, factor] |
census_tr_certainty_2010 |
1. complete_and_valid.1
2. ZIP_4.2
3. ZIP_code_only.4
4. ZIP_code_of_PO_Box.5
5. Not_assigned.9 |
| 2136 | ( | 98.6% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 24 | ( | 1.1% | ) | | 2 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2010
[factor] |
1. complete_and_valid.1
2. ZIP_4.2
3. ZIP_code_only.4
4. ZIP_code_of_PO_Box.5
5. Not_assigned.9 |
| 182 | ( | 96.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.2% | ) | | 1 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2010
[factor] |
1. complete_and_valid.1
2. ZIP_4.2
3. ZIP_code_only.4
4. ZIP_code_of_PO_Box.5
5. Not_assigned.9 |
| 167 | ( | 96.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2010
[factor] |
1. complete_and_valid.1
2. ZIP_4.2
3. ZIP_code_only.4
4. ZIP_code_of_PO_Box.5
5. Not_assigned.9 |
| 228 | ( | 96.2% | ) | | 2 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.5% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2010
[factor] |
1. complete_and_valid.1
2. ZIP_4.2
3. ZIP_code_only.4
4. ZIP_code_of_PO_Box.5
5. Not_assigned.9 |
| 173 | ( | 99.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2010
[factor] |
1. complete_and_valid.1
2. ZIP_4.2
3. ZIP_code_only.4
4. ZIP_code_of_PO_Box.5
5. Not_assigned.9 |
| 288 | ( | 99.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2010
[factor] |
1. complete_and_valid.1
2. ZIP_4.2
3. ZIP_code_only.4
4. ZIP_code_of_PO_Box.5
5. Not_assigned.9 |
| 1082 | ( | 99.5% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
census_tr_certainty_2010
[factor] |
1. complete_and_valid.1
2. ZIP_4.2
3. ZIP_code_only.4
4. ZIP_code_of_PO_Box.5
5. Not_assigned.9 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SEQUENCE NUMBER–CENTRAL
Description: Code indicates the sequence of all reportable neoplasms over the lifetime of the person. This data item differs from Sequence Number-Hospital [560], because the definitions of reportable neoplasms often vary between a hospital and a central registry. Each neoplasm is assigned a different number. Sequence Number 00 indicates that the person has had only one in situ or one malignant neoplasm as defined by the Federal reportable list (regardless of central registry reference date). Sequence Number 01 indicates the first of two or more reportable neoplasms, but 02 indicates the second of two or more reportable neoplasms, and so on. Because the time period of Sequence Number is a person’s lifetime, reportable neoplasms not included in the central registry (those that occur outside the registry catchment area or before the reference date) also are allotted a sequence number. For example, a registry may contain a single record for a patient with a sequence number of 02 because the first reportable neoplasm preceded the central registry’s reference date.
Reporting Requirements: Federally Required and State/Province Defined: The Federal or SEER/NPCR standard defining the reportable neoplasms is described in Chapter III, Standards For Tumor Inclusion and Reportability. It is assumed that this shared standard is the “minimum” definition of reportability. Individual central cancer registries may define additional neoplasms as reportable.
Numeric codes in the 00-59 range indicate the sequence of neoplasms of in situ or malignant behavior (2 or 3) at the time of diagnosis, which SEER/NPCR standards require to be reported. Codes 60 to 87 indicate the sequence of non-malignant tumors (as defined in Chapter III) and any other neoplasms that the central registry has defined as reportable. Neoplasms required by SEER/NPCR with an in situ or malignant behavior at the time of diagnosis are sequenced completely independently of this higher-numbered category. Sequence Number-Hospital does not affect Sequence Number-Central. The two notational systems are independent but central registries should take Sequence Number-Hospital [560] into account when coding Sequence Number Central.
Rationale: The purpose of sequencing based on the patient’s lifetime is to truly identify the 00s, the people who only had one malignant primary in their lifetimes for survival analysis. If a central registry sequences by just what is reported to them, then it will be unclear whether 00 means the person only had one malignant primary in his lifetime or the person had one malignant primary since the central registry started collecting data. The Federally required reportable list has changed throughout the years, so the registry must use the appropriate reportable list for the year of diagnosis. The central registry reference date will not affect Sequence Number-Central.
Codes
- 00 One primary in the patient’s lifetime
- 01 First of two or more primaries
- 02 Second of two or more primaries
- 03 Third of two or more primaries
- 04 Forth of two or more primaries
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#380
All data
st_css() #IMPORTANT!
sequencenumbercentral <- as.factor(trimws(d[,"sequencenumbercentral"]))
levels(sequencenumbercentral)[levels(sequencenumbercentral)=="0"] <- "One_primary.0"
levels(sequencenumbercentral)[levels(sequencenumbercentral)=="1"] <- "First_of_two_or_more.1"
levels(sequencenumbercentral)[levels(sequencenumbercentral)=="2"] <- "Second_of_two_or_more.2"
levels(sequencenumbercentral)[levels(sequencenumbercentral)=="3"] <- "Third_of_two_or_more.2"
levels(sequencenumbercentral)[levels(sequencenumbercentral)=="4"] <- "Forth_of_two_or_more.4"
new.d <- data.frame(new.d, sequencenumbercentral)
new.d <- apply_labels(new.d, sequencenumbercentral = "sequence_number_central")
temp.d <- data.frame (new.d.1, sequencenumbercentral)
summarytools::view(dfSummary(new.d$sequencenumbercentral, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sequencenumbercentral
[labelled, factor] |
sequence_number_central |
1. One_primary.0
2. First_of_two_or_more.1
3. Second_of_two_or_more.2
4. Third_of_two_or_more.2
5. Forth_of_two_or_more.4 |
| 1997 | ( | 92.2% | ) | | 49 | ( | 2.3% | ) | | 105 | ( | 4.8% | ) | | 14 | ( | 0.6% | ) | | 2 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sequence_number_central
[factor] |
1. One_primary.0
2. First_of_two_or_more.1
3. Second_of_two_or_more.2
4. Third_of_two_or_more.2
5. Forth_of_two_or_more.4 |
| 178 | ( | 94.2% | ) | | 4 | ( | 2.1% | ) | | 5 | ( | 2.6% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sequence_number_central
[factor] |
1. One_primary.0
2. First_of_two_or_more.1
3. Second_of_two_or_more.2
4. Third_of_two_or_more.2
5. Forth_of_two_or_more.4 |
| 161 | ( | 92.5% | ) | | 5 | ( | 2.9% | ) | | 6 | ( | 3.4% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sequence_number_central
[factor] |
1. One_primary.0
2. First_of_two_or_more.1
3. Second_of_two_or_more.2
4. Third_of_two_or_more.2
5. Forth_of_two_or_more.4 |
| 224 | ( | 94.5% | ) | | 2 | ( | 0.8% | ) | | 11 | ( | 4.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sequence_number_central
[factor] |
1. One_primary.0
2. First_of_two_or_more.1
3. Second_of_two_or_more.2
4. Third_of_two_or_more.2
5. Forth_of_two_or_more.4 |
| 152 | ( | 87.4% | ) | | 4 | ( | 2.3% | ) | | 13 | ( | 7.5% | ) | | 4 | ( | 2.3% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sequence_number_central
[factor] |
1. One_primary.0
2. First_of_two_or_more.1
3. Second_of_two_or_more.2
4. Third_of_two_or_more.2
5. Forth_of_two_or_more.4 |
| 273 | ( | 94.1% | ) | | 3 | ( | 1.0% | ) | | 12 | ( | 4.1% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sequence_number_central
[factor] |
1. One_primary.0
2. First_of_two_or_more.1
3. Second_of_two_or_more.2
4. Third_of_two_or_more.2
5. Forth_of_two_or_more.4 |
| 994 | ( | 91.4% | ) | | 30 | ( | 2.8% | ) | | 58 | ( | 5.3% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
sequence_number_central
[factor] |
1. One_primary.0
2. First_of_two_or_more.1
3. Second_of_two_or_more.2
4. Third_of_two_or_more.2
5. Forth_of_two_or_more.4 |
| 15 | ( | 93.8% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DATE OF DIAGNOSIS
Date of initial diagnosis by a recognized medical practitioner for the tumor being reported whether clinically or microscopically confirmed. See Chapter X for date format.
For more discussion on determining date of diagnosis, consult the SEER Program Coding and Staging Manual or CoC STORE manual.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=21#390
All data
dateofdiagnosis <- trimws(d[,"dateofdiagnosis"])
#new.d.n <- data.frame(new.d.n, dateofdiagnosis) # keep NAACCR coding
select99 <- substr(dateofdiagnosis, start=7, stop=8)=="99"
dateofdiagnosis[select99] <- substr(dateofdiagnosis[select99], start=1, stop=6)
select6 <- nchar(trimws(dateofdiagnosis))==6
dateofdiagnosis[select6] <- as.Date(as.yearmon(dateofdiagnosis[select6], c("%Y%m")))
select8 <- nchar(trimws(dateofdiagnosis))==8
dateofdiagnosis[select8] <- as.Date(dateofdiagnosis[select8], c("%Y%m%d"))
select4 <- nchar(trimws(dateofdiagnosis))==4
dateofdiagnosis[select4] <- as.Date(dateofdiagnosis[select4], c("%Y"))
dateofdiagnosis <- as.Date(as.numeric(dateofdiagnosis), origin = "1970-01-01")
new.d <- data.frame(new.d, dateofdiagnosis)
new.d <- apply_labels(new.d, dateofdiagnosis = "Date of Diagnosis")
temp.d <- data.frame (new.d.1, dateofdiagnosis)
summarytools::view(dfSummary(new.d$dateofdiagnosis, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
dateofdiagnosis
[labelled, Date] |
Date of Diagnosis |
min : 2011-08-01
med : 2016-01-26
max : 2018-12-01
range : 7y 4m 0d |
533 distinct values |
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_diagnosis
[Date] |
1. 2015-01-01
2. 2015-02-01
3. 2015-03-01
4. 2015-04-01
5. 2015-05-01
6. 2015-06-01
7. 2015-07-01
8. 2015-08-01
9. 2015-09-01
10. 2015-10-01
11. 2015-11-01
12. 2015-12-01
13. 2016-01-01
14. 2016-02-01
15. 2016-03-01
16. 2016-04-01
17. 2016-05-01
18. 2016-06-01
19. 2016-07-01
20. 2016-08-01
21. 2016-09-01
22. 2016-10-01
23. 2016-11-01
24. 2016-12-01
25. 2017-02-01
26. 2017-03-01
27. 2017-06-01
28. 2017-08-01
29. 2017-11-01
30. 2017-12-01 |
| 8 | ( | 4.2% | ) | | 4 | ( | 2.1% | ) | | 9 | ( | 4.8% | ) | | 9 | ( | 4.8% | ) | | 4 | ( | 2.1% | ) | | 7 | ( | 3.7% | ) | | 10 | ( | 5.3% | ) | | 12 | ( | 6.3% | ) | | 6 | ( | 3.2% | ) | | 3 | ( | 1.6% | ) | | 9 | ( | 4.8% | ) | | 9 | ( | 4.8% | ) | | 6 | ( | 3.2% | ) | | 5 | ( | 2.6% | ) | | 13 | ( | 6.9% | ) | | 15 | ( | 7.9% | ) | | 9 | ( | 4.8% | ) | | 8 | ( | 4.2% | ) | | 5 | ( | 2.6% | ) | | 10 | ( | 5.3% | ) | | 5 | ( | 2.6% | ) | | 2 | ( | 1.1% | ) | | 6 | ( | 3.2% | ) | | 4 | ( | 2.1% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 3 | ( | 1.6% | ) | | 1 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_diagnosis
[Date] |
min : 2015-01-05
med : 2016-01-22
max : 2017-09-11
range : 2y 8m 6d |
146 distinct values |
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_diagnosis
[Date] |
min : 2015-01-01
med : 2015-11-01
max : 2016-12-01
range : 1y 11m 0d |
24 distinct values |
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_diagnosis
[Date] |
min : 2015-01-01
med : 2016-08-01
max : 2018-12-01
range : 3y 11m 0d |
48 distinct values |
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_diagnosis
[Date] |
min : 2015-01-01
med : 2016-03-22
max : 2016-12-30
range : 1y 11m 29d |
216 distinct values |
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_diagnosis
[Date] |
min : 2015-01-01
med : 2016-01-19
max : 2018-09-26
range : 3y 8m 25d |
458 distinct values |
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_of_diagnosis
[Date] |
min : 2011-08-01
med : 2013-05-01
max : 2015-04-01
range : 3y 8m 0d |
15 distinct values |
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
PRIMARY SITE
Code for the primary site of the tumor being reported using either ICD-O-2 or ICD-O-3. NAACCR adopted ICD-O-2 as the standard coding system for tumors diagnosed beginning January 1, 1992. In addition, NAACCR recommended that tumors diagnosed prior to 1992 be converted to ICD-O-2. The topography (primary site) codes did not change between ICD-O-2 and ICD-O-3.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#400
primarysite <- as.factor(d[,"primarysite"])
levels(primarysite) <- list(Prostate="C619") # other codings become NA
new.d <- data.frame(new.d, primarysite)
new.d <- apply_labels(new.d, primarysite = "Primary Tumor Site")
#cro(new.d$primarysite)# this is pretty but doesn't show NAs
primarysite<-count(new.d$primarysite)
colnames(primarysite)<- c("Primary site", "Total")
kable(primarysite, format = "simple", align = 'l', caption = "Overview of 8 Registries")
Overview of 8 Registries
| Prostate |
2166 |
| NA |
1 |
GRADE
Code for the grade or degree of differentiation of the reportable tumor. For lymphomas and leukemias, field also is used to indicate T-, B-, Null-, or NK-cell origin.
See the grade tables on page 67 of ICD-O-3.16 See also the most recent CoC STORE manual and SEER Program Code Manual, for site specific coding rules and conversions.
- Grade I
- Grade II
- Grade III
- Grade IV
- T-cell
- B-cell
- Null cell
- NK (natural killer) cell
- Grade/differentiation unknown, not stated, or not applicable
Comment: Use the most recent Hematopoietic and Lymphoid rules for assigning grades 5-8.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#440
All data
grade <- as.factor(d[,"grade"])
levels(grade) <- list(Grade_I.1="1",
Grade_II.2="2",
Grade_III.3="3",
Grade_IV.4="4",
T_cell.5="5",
B_cell.6="6",
Null_cell.7="7",
NK_cell.8="8",
Unknown.9="9")
new.d <- data.frame(new.d, grade)
new.d <- apply_labels(new.d, grade = "Tumor Grade")
#cro(new.d$grade)# this is pretty but doesn't show NAs
#summary(new.d$grade)
temp.d <- data.frame (new.d.1, grade)
summarytools::view(dfSummary(new.d$grade, style = 'grid', max.distinct.values = 10, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade
[labelled, factor] |
Tumor Grade |
1. Grade_I.1
2. Grade_II.2
3. Grade_III.3
4. Grade_IV.4
5. T_cell.5
6. B_cell.6
7. Null_cell.7
8. NK_cell.8
9. Unknown.9 |
| 375 | ( | 17.6% | ) | | 1156 | ( | 54.2% | ) | | 589 | ( | 27.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 13 | ( | 0.6% | ) |
|
 |
34
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade
[factor] |
1. Grade_I.1
2. Grade_II.2
3. Grade_III.3
4. Grade_IV.4
5. T_cell.5
6. B_cell.6
7. Null_cell.7
8. NK_cell.8
9. Unknown.9 |
| 40 | ( | 21.2% | ) | | 108 | ( | 57.1% | ) | | 39 | ( | 20.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade
[factor] |
1. Grade_I.1
2. Grade_II.2
3. Grade_III.3
4. Grade_IV.4
5. T_cell.5
6. B_cell.6
7. Null_cell.7
8. NK_cell.8
9. Unknown.9 |
| 57 | ( | 32.8% | ) | | 93 | ( | 53.4% | ) | | 23 | ( | 13.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade
[factor] |
1. Grade_I.1
2. Grade_II.2
3. Grade_III.3
4. Grade_IV.4
5. T_cell.5
6. B_cell.6
7. Null_cell.7
8. NK_cell.8
9. Unknown.9 |
| 47 | ( | 19.8% | ) | | 120 | ( | 50.6% | ) | | 68 | ( | 28.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade
[factor] |
1. Grade_I.1
2. Grade_II.2
3. Grade_III.3
4. Grade_IV.4
5. T_cell.5
6. B_cell.6
7. Null_cell.7
8. NK_cell.8
9. Unknown.9 |
| 17 | ( | 12.0% | ) | | 83 | ( | 58.5% | ) | | 41 | ( | 28.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade
[factor] |
1. Grade_I.1
2. Grade_II.2
3. Grade_III.3
4. Grade_IV.4
5. T_cell.5
6. B_cell.6
7. Null_cell.7
8. NK_cell.8
9. Unknown.9 |
| 27 | ( | 9.3% | ) | | 153 | ( | 52.8% | ) | | 110 | ( | 37.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade
[factor] |
1. Grade_I.1
2. Grade_II.2
3. Grade_III.3
4. Grade_IV.4
5. T_cell.5
6. B_cell.6
7. Null_cell.7
8. NK_cell.8
9. Unknown.9 |
| 185 | ( | 17.1% | ) | | 592 | ( | 54.6% | ) | | 301 | ( | 27.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 0.6% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade
[factor] |
1. Grade_I.1
2. Grade_II.2
3. Grade_III.3
4. Grade_IV.4
5. T_cell.5
6. B_cell.6
7. Null_cell.7
8. NK_cell.8
9. Unknown.9 |
| 2 | ( | 12.5% | ) | | 7 | ( | 43.8% | ) | | 7 | ( | 43.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DIAGNOSTIC CONFIRMATION
- Description: Code for the best method of diagnostic confirmation of the cancer being reported at any time in the patient’s history.
- Rationale: Diagnostic confirmation is useful to calculate rates based on microscopically confirmed cancers. Full incidence calculations must also include tumors that are only confirmed clinically. The percentage of tumors that not micropscopically confirmed is an indication of whether case finding is including sources outside of pathology reports.
- Codes
- 1 Positive histology
- 2 Positive cytology
- 3 Positive histology PLUS - positive immunophenotyping AND/OR positive genetic studies (Used only for hematopoietic and lymphoid neoplasms M-9590/3-9992/3)
- 4 Positive microscopic confirmation, method not specified
- 5 Positive laboratory test/marker study
- 6 Direct visualization without microscopic confirmation
- 7 Radiography and/or other imaging techniques without microscopic confirmation
- 8 Clinical diagnosis only (other than 5, 6, or 7)
- 9 Unknown whether or not microscopically confirmed; death certificate only
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#490
All data
st_css() #IMPORTANT!
diagnosticconfirmation <- as.factor(trimws(d[,"diagnosticconfirmation"]))
levels(diagnosticconfirmation)[levels(diagnosticconfirmation)=="1"] <- "Positive_histology.1"
new.d <- data.frame(new.d, diagnosticconfirmation)
new.d <- apply_labels(new.d, diagnosticconfirmation = "diagnostic_confirmation")
temp.d <- data.frame (new.d.1, diagnosticconfirmation)
summarytools::view(dfSummary(new.d$diagnosticconfirmation, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
diagnosticconfirmation
[labelled, factor] |
diagnostic_confirmation |
1. Positive_histology.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
diagnostic_confirmation
[factor] |
1. Positive_histology.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
diagnostic_confirmation
[factor] |
1. Positive_histology.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
diagnostic_confirmation
[factor] |
1. Positive_histology.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
diagnostic_confirmation
[factor] |
1. Positive_histology.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
diagnostic_confirmation
[factor] |
1. Positive_histology.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
diagnostic_confirmation
[factor] |
1. Positive_histology.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
diagnostic_confirmation
[factor] |
1. Positive_histology.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TYPE OF REPORTING SOURCE
- This variable codes the source documents used to abstract the majority of information on the tumor being reported. This may not be the source of original case finding (for example, if a case is identified through a pathology laboratory report review and all source documents used to abstract the case are from the physician’s office, code this item 4).
Rationale: The code in this field can be used to explain why information may be incomplete on a tumor. For example, death certificate only cases have unknown values for many data items, so one may want to exclude them from some analyses. The field also is used to monitor the success of non-hospital case reporting and follow-back mechanisms. All population-based registries should have some death certificate-only cases where no hospital admission was involved, but too high a percentage can imply both shortcomings in case-finding and that follow-back to uncover missed hospital reports was not complete.
Coding Instructions: Code in the following priority order: 1, 2, 8, 4, 3, 5, 6, 7. This is a change to reflect the addition of codes 2 and 8 and to prioritize laboratory reports over nursing home reports. The source facilities included in the previous code 1 (hospital inpatient and outpatient) are split between codes 1, 2, and 8.
This data item is intended to indicate the completeness of information available to the abstractor. Reports from health plans (e.g., Kaiser, Veterans Administration, military facilities) in which all diagnostic and treatment information is maintained centrally and is available to the abstractor are expected to be at least as complete as reports for hospital inpatients, which is why these sources are grouped with inpatients and given the code with the highest priority.
Sources coded with ‘2’ usually have complete information on the cancer diagnosis, staging, and treatment.
Sources coded with ‘8’ would include, but would not be limited to, outpatient surgery and nuclear medicine services. A physician’s office that calls itself a surgery center should be coded as a physician’s office. Surgery centers are equipped and staffed to perform surgical procedures under general anesthesia. If a physician’s office calls itself a surgery center, but cannot perform surgical procedures under general anesthesia, code as a physician office.
Codes
- 1 Hospital inpatient; Managed health plans with comprehensive, unified medical records
- 2 Radiation Treatment Centers or Medical Oncology Centers (hospital-affiliated or independent)
- 3 Laboratory only (hospital-affiliated or independent)
- 4 Physician’s office/private medical practitioner (LMD)
- 5 Nursing/convalescent home/hospice
- 6 Autopsy only
- 7 Death certificate only
- 8 Other hospital outpatient units/surgery centers
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#500
All data
typeofreportingsource <- as.factor(d[,"typeofreportingsource"])
levels(typeofreportingsource) <- list(Hospital.1="1",
Radiation_Tx.2="2",
Laboratory_Only.3="3",
Physician.4="4",
Nursing.5="5",
Autopsy.6="6",
Death_Certificate.7="7",
Other_Hospital.Unit.8="8")
new.d <- data.frame(new.d, typeofreportingsource)
new.d <- apply_labels(new.d, typeofreportingsource = "Source of Tumor Information")
#summary(new.d$typeofreportingsource)
temp.d <- data.frame (new.d.1, typeofreportingsource)
summarytools::view(dfSummary(new.d$typeofreportingsource, style = 'grid', max.distinct.values = 10, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
typeofreportingsource
[labelled, factor] |
Source of Tumor Information |
1. Hospital.1
2. Radiation_Tx.2
3. Laboratory_Only.3
4. Physician.4
5. Nursing.5
6. Autopsy.6
7. Death_Certificate.7
8. Other_Hospital.Unit.8 |
| 1992 | ( | 91.9% | ) | | 66 | ( | 3.0% | ) | | 53 | ( | 2.4% | ) | | 30 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 26 | ( | 1.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
type_of_reporting_source
[factor] |
1. Hospital.1
2. Radiation_Tx.2
3. Laboratory_Only.3
4. Physician.4
5. Nursing.5
6. Autopsy.6
7. Death_Certificate.7
8. Other_Hospital.Unit.8 |
| 186 | ( | 98.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
type_of_reporting_source
[factor] |
1. Hospital.1
2. Radiation_Tx.2
3. Laboratory_Only.3
4. Physician.4
5. Nursing.5
6. Autopsy.6
7. Death_Certificate.7
8. Other_Hospital.Unit.8 |
| 169 | ( | 97.1% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
type_of_reporting_source
[factor] |
1. Hospital.1
2. Radiation_Tx.2
3. Laboratory_Only.3
4. Physician.4
5. Nursing.5
6. Autopsy.6
7. Death_Certificate.7
8. Other_Hospital.Unit.8 |
| 228 | ( | 96.2% | ) | | 6 | ( | 2.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
type_of_reporting_source
[factor] |
1. Hospital.1
2. Radiation_Tx.2
3. Laboratory_Only.3
4. Physician.4
5. Nursing.5
6. Autopsy.6
7. Death_Certificate.7
8. Other_Hospital.Unit.8 |
| 169 | ( | 97.1% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.3% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
type_of_reporting_source
[factor] |
1. Hospital.1
2. Radiation_Tx.2
3. Laboratory_Only.3
4. Physician.4
5. Nursing.5
6. Autopsy.6
7. Death_Certificate.7
8. Other_Hospital.Unit.8 |
| 255 | ( | 87.9% | ) | | 8 | ( | 2.8% | ) | | 11 | ( | 3.8% | ) | | 16 | ( | 5.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
type_of_reporting_source
[factor] |
1. Hospital.1
2. Radiation_Tx.2
3. Laboratory_Only.3
4. Physician.4
5. Nursing.5
6. Autopsy.6
7. Death_Certificate.7
8. Other_Hospital.Unit.8 |
| 970 | ( | 89.2% | ) | | 49 | ( | 4.5% | ) | | 38 | ( | 3.5% | ) | | 5 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 25 | ( | 2.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
type_of_reporting_source
[factor] |
1. Hospital.1
2. Radiation_Tx.2
3. Laboratory_Only.3
4. Physician.4
5. Nursing.5
6. Autopsy.6
7. Death_Certificate.7
8. Other_Hospital.Unit.8 |
| 15 | ( | 93.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
HISTOLOGIC TYPE ICD-O-3
All data
st_css() #IMPORTANT!
histologictypeicdo3 <- as.factor(trimws(d[,"histologictypeicdo3"]))
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8000"] <- "Neoplasm_malignant.8000"
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8010"] <- "Carcinoma_NOS.8010"
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8140"] <- "Adenocarcinoma_NOS.8140"
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8323"] <- "Mixed_cell_adenocarcinoma.8323"
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8480"] <- "Mucinous_adenocarcinoma.8480 "
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8481"] <- "Mucin_producing_adenocarcinoma.8481"
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8490"] <- "Signet_ring_cell_adenocarcinoma.8490"
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8500"] <- "Invasive_breast_carcinoma.8500"
levels(histologictypeicdo3)[levels(histologictypeicdo3)=="8550"] <- "Acinar_cell_tumor.8550"
new.d <- data.frame(new.d, histologictypeicdo3)
new.d <- apply_labels(new.d, histologictypeicdo3 = "histologic_type_icdo3")
temp.d <- data.frame (new.d.1, histologictypeicdo3)
summarytools::view(dfSummary(new.d$histologictypeicdo3, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
histologictypeicdo3
[labelled, factor] |
histologic_type_icdo3 |
1. Neoplasm_malignant.8000
2. Carcinoma_NOS.8010
3. Adenocarcinoma_NOS.8140
4. Mixed_cell_adenocarcinoma
5. Mucinous_adenocarcinoma.8
6. Mucin_producing_adenocarc
7. Signet_ring_cell_adenocar
8. Invasive_breast_carcinoma
9. Acinar_cell_tumor.8550 |
| 5 | ( | 0.2% | ) | | 5 | ( | 0.2% | ) | | 2138 | ( | 98.7% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 10 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
histologic_type_icdo3
[factor] |
1. Neoplasm_malignant.8000
2. Carcinoma_NOS.8010
3. Adenocarcinoma_NOS.8140
4. Mixed_cell_adenocarcinoma
5. Mucinous_adenocarcinoma.8
6. Mucin_producing_adenocarc
7. Signet_ring_cell_adenocar
8. Invasive_breast_carcinoma
9. Acinar_cell_tumor.8550 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 186 | ( | 98.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
histologic_type_icdo3
[factor] |
1. Neoplasm_malignant.8000
2. Carcinoma_NOS.8010
3. Adenocarcinoma_NOS.8140
4. Mixed_cell_adenocarcinoma
5. Mucinous_adenocarcinoma.8
6. Mucin_producing_adenocarc
7. Signet_ring_cell_adenocar
8. Invasive_breast_carcinoma
9. Acinar_cell_tumor.8550 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
histologic_type_icdo3
[factor] |
1. Neoplasm_malignant.8000
2. Carcinoma_NOS.8010
3. Adenocarcinoma_NOS.8140
4. Mixed_cell_adenocarcinoma
5. Mucinous_adenocarcinoma.8
6. Mucin_producing_adenocarc
7. Signet_ring_cell_adenocar
8. Invasive_breast_carcinoma
9. Acinar_cell_tumor.8550 |
| 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 235 | ( | 99.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
histologic_type_icdo3
[factor] |
1. Neoplasm_malignant.8000
2. Carcinoma_NOS.8010
3. Adenocarcinoma_NOS.8140
4. Mixed_cell_adenocarcinoma
5. Mucinous_adenocarcinoma.8
6. Mucin_producing_adenocarc
7. Signet_ring_cell_adenocar
8. Invasive_breast_carcinoma
9. Acinar_cell_tumor.8550 |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 171 | ( | 98.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
histologic_type_icdo3
[factor] |
1. Neoplasm_malignant.8000
2. Carcinoma_NOS.8010
3. Adenocarcinoma_NOS.8140
4. Mixed_cell_adenocarcinoma
5. Mucinous_adenocarcinoma.8
6. Mucin_producing_adenocarc
7. Signet_ring_cell_adenocar
8. Invasive_breast_carcinoma
9. Acinar_cell_tumor.8550 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 285 | ( | 98.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
histologic_type_icdo3
[factor] |
1. Neoplasm_malignant.8000
2. Carcinoma_NOS.8010
3. Adenocarcinoma_NOS.8140
4. Mixed_cell_adenocarcinoma
5. Mucinous_adenocarcinoma.8
6. Mucin_producing_adenocarc
7. Signet_ring_cell_adenocar
8. Invasive_breast_carcinoma
9. Acinar_cell_tumor.8550 |
| 4 | ( | 0.4% | ) | | 4 | ( | 0.4% | ) | | 1071 | ( | 98.5% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 6 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
histologic_type_icdo3
[factor] |
1. Neoplasm_malignant.8000
2. Carcinoma_NOS.8010
3. Adenocarcinoma_NOS.8140
4. Mixed_cell_adenocarcinoma
5. Mucinous_adenocarcinoma.8
6. Mucin_producing_adenocarc
7. Signet_ring_cell_adenocar
8. Invasive_breast_carcinoma
9. Acinar_cell_tumor.8550 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
BEHAVIOR CODE ICD-O-3
Description: Code for the behavior of the tumor being reported using ICD-O-3. NAACCR adopted ICD-O-3 as the standard coding system for tumors diagnosed beginning January 1, 2001, and later recommended that prior cases be converted from ICD-O-2. See Behavior (92-00) ICD-O-2 [430], for ICD-O-2 codes.
Juvenile astrocytoma is coded as borderline in ICD-O-3; North American registries report as 9421/3. Clarification of Required Status Behavior is required by all standard-setting organizations for tumors diagnosed on or after January 1, 2001, and recommended (by conversion from ICD-O-2 codes) for tumors diagnosed before 2001.
When the histologic type is coded according to the ICD-O-3, the histology code must be reported in Histologic Type ICD-O-3 [522], with behavior coded in Behavior Code ICD-O-3 [523].
For information on required status for related data items for histologic type and behavior when coded according to ICD-O-2, see Histology (92-00) ICD-O-2 [420] and Behavior (92-00) ICD-O-2 [430].
Codes
- Valid codes are 0-3. See ICD-O-3,14 page 66, for behavior codes and definitions.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#523
All data
st_css() #IMPORTANT!
behaviorcodeicdo3 <- as.factor(trimws(d[,"behaviorcodeicdo3"]))
new.d <- data.frame(new.d, behaviorcodeicdo3)
new.d <- apply_labels(new.d, behaviorcodeicdo3 = "behavior_code_icdo3")
temp.d <- data.frame (new.d.1, behaviorcodeicdo3)
summarytools::view(dfSummary(new.d$behaviorcodeicdo3, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
behaviorcodeicdo3
[labelled, factor] |
behavior_code_icdo3 |
1. 3 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
PRIMARY PAYER AT DX
Description: Primary payer/insurance carrier at the time of initial diagnosis and/or treatment at the reporting facility.
Rationale: This item is used in financial analysis and as an indicator for quality and outcome analyses.
-Codes
+ 01 Not insured
+ 02 Not insured, self-pay
+ 10 Insurance, NOS
+ 20 Private Insurance: Managed care, HMO, or PPO
+ 21 Private Insurance: Fee-for-Service
+ 31 Medicaid
+ 35 Medicaid - Administered through a Managed Care plan
+ 60 Medicare/Medicare, NOS
+ 61 Medicare with supplement, NOS
+ 62 Medicare - Administered through a Managed Care plan
+ 63 Medicare with private supplement
+ 64 Medicare with Medicaid eligibility
+ 65 TRICARE
+ 66 Military
+ 67 Veterans Affairs
+ 68 Indian/Public Health Service
+ 99 Insurance status unknown
All data
primarypayeratdx <- as.factor(d[,"primarypayeratdx"])
levels(primarypayeratdx) <- list(Not_insured.1="1",
Not_insured_self_pay.2="2",
Insurance.10="10",
Private_managed.20="20",
Private_FFS.21="21", # fee-for-service
Medicaid.31="31",
Medicaid_managed_care.35="35",
Medicare_medicare.60="60",
Medicare_suppl.61="61",
Medicare_managed_care.62="62",
Medicare_private_suppl.63="63",
Medicare_medicaid.64="64",
TRICARE.65="65",
Military.66="66",
Veterans_Affairs.67="67",
Indian_PHS.68="68",
Unknown.99="99")
primarypayeratdx <- relevel(primarypayeratdx, ref="Private_managed.20")
new.d <- data.frame(new.d, primarypayeratdx)
new.d <- apply_labels(new.d, primarypayeratdx = "Primary payer/insurance at the time of diagnosis")
#summary(new.d$primarypayeratdx)
temp.d <- data.frame (new.d.1, primarypayeratdx)
summarytools::view(dfSummary(new.d$primarypayeratdx, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
primarypayeratdx
[labelled, factor] |
Primary payer/insurance at the time of
diagnosis |
1. Private_managed.20
2. Not_insured.1
3. Not_insured_self_pay.2
4. Insurance.10
5. Private_FFS.21
6. Medicaid.31
7. Medicaid_managed_care.35
8. Medicare_medicare.60
9. Medicare_suppl.61
10. Medicare_managed_care.62
11. Medicare_private_suppl.63
12. Medicare_medicaid.64
13. TRICARE.65
14. Military.66
15. Veterans_Affairs.67
16. Indian_PHS.68
17. Unknown.99 |
| 894 | ( | 42.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 46 | ( | 2.2% | ) | | 36 | ( | 1.7% | ) | | 75 | ( | 3.5% | ) | | 62 | ( | 2.9% | ) | | 195 | ( | 9.2% | ) | | 111 | ( | 5.2% | ) | | 196 | ( | 9.2% | ) | | 120 | ( | 5.6% | ) | | 55 | ( | 2.6% | ) | | 47 | ( | 2.2% | ) | | 1 | ( | 0.0% | ) | | 149 | ( | 7.0% | ) | | 0 | ( | 0.0% | ) | | 138 | ( | 6.5% | ) |
|
 |
42
(1.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
primary_payer_at_dx
[factor] |
1. Private_managed.20
2. Not_insured.1
3. Not_insured_self_pay.2
4. Insurance.10
5. Private_FFS.21
6. Medicaid.31
7. Medicaid_managed_care.35
8. Medicare_medicare.60
9. Medicare_suppl.61
10. Medicare_managed_care.62
11. Medicare_private_suppl.63
12. Medicare_medicaid.64
13. TRICARE.65
14. Military.66
15. Veterans_Affairs.67
16. Indian_PHS.68
17. Unknown.99 |
| 114 | ( | 60.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 3.7% | ) | | 6 | ( | 3.2% | ) | | 14 | ( | 7.4% | ) | | 1 | ( | 0.5% | ) | | 23 | ( | 12.2% | ) | | 4 | ( | 2.1% | ) | | 4 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.1% | ) |
|
 |
1
(0.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
primary_payer_at_dx
[factor] |
1. Private_managed.20
2. Not_insured.1
3. Not_insured_self_pay.2
4. Insurance.10
5. Private_FFS.21
6. Medicaid.31
7. Medicaid_managed_care.35
8. Medicare_medicare.60
9. Medicare_suppl.61
10. Medicare_managed_care.62
11. Medicare_private_suppl.63
12. Medicare_medicaid.64
13. TRICARE.65
14. Military.66
15. Veterans_Affairs.67
16. Indian_PHS.68
17. Unknown.99 |
| 110 | ( | 63.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.5% | ) | | 4 | ( | 2.3% | ) | | 17 | ( | 9.8% | ) | | 4 | ( | 2.3% | ) | | 10 | ( | 5.8% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 5.2% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) |
|
 |
1
(0.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
primary_payer_at_dx
[factor] |
1. Private_managed.20
2. Not_insured.1
3. Not_insured_self_pay.2
4. Insurance.10
5. Private_FFS.21
6. Medicaid.31
7. Medicaid_managed_care.35
8. Medicare_medicare.60
9. Medicare_suppl.61
10. Medicare_managed_care.62
11. Medicare_private_suppl.63
12. Medicare_medicaid.64
13. TRICARE.65
14. Military.66
15. Veterans_Affairs.67
16. Indian_PHS.68
17. Unknown.99 |
| 135 | ( | 57.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) | | 1 | ( | 0.4% | ) | | 11 | ( | 4.6% | ) | | 11 | ( | 4.6% | ) | | 15 | ( | 6.3% | ) | | 21 | ( | 8.9% | ) | | 14 | ( | 5.9% | ) | | 2 | ( | 0.8% | ) | | 4 | ( | 1.7% | ) | | 4 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 13 | ( | 5.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
primary_payer_at_dx
[factor] |
1. Private_managed.20
2. Not_insured.1
3. Not_insured_self_pay.2
4. Insurance.10
5. Private_FFS.21
6. Medicaid.31
7. Medicaid_managed_care.35
8. Medicare_medicare.60
9. Medicare_suppl.61
10. Medicare_managed_care.62
11. Medicare_private_suppl.63
12. Medicare_medicaid.64
13. TRICARE.65
14. Military.66
15. Veterans_Affairs.67
16. Indian_PHS.68
17. Unknown.99 |
| 56 | ( | 33.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 3.0% | ) | | 2 | ( | 1.2% | ) | | 7 | ( | 4.1% | ) | | 16 | ( | 9.5% | ) | | 15 | ( | 8.9% | ) | | 4 | ( | 2.4% | ) | | 22 | ( | 13.0% | ) | | 26 | ( | 15.4% | ) | | 5 | ( | 3.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 4.7% | ) |
|
 |
5
(2.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
primary_payer_at_dx
[factor] |
1. Private_managed.20
2. Not_insured.1
3. Not_insured_self_pay.2
4. Insurance.10
5. Private_FFS.21
6. Medicaid.31
7. Medicaid_managed_care.35
8. Medicare_medicare.60
9. Medicare_suppl.61
10. Medicare_managed_care.62
11. Medicare_private_suppl.63
12. Medicare_medicaid.64
13. TRICARE.65
14. Military.66
15. Veterans_Affairs.67
16. Indian_PHS.68
17. Unknown.99 |
| 110 | ( | 39.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) | | 1 | ( | 0.4% | ) | | 10 | ( | 3.6% | ) | | 14 | ( | 5.0% | ) | | 26 | ( | 9.3% | ) | | 17 | ( | 6.0% | ) | | 36 | ( | 12.8% | ) | | 8 | ( | 2.8% | ) | | 6 | ( | 2.1% | ) | | 3 | ( | 1.1% | ) | | 1 | ( | 0.4% | ) | | 33 | ( | 11.7% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 3.6% | ) |
|
 |
9
(3.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
primary_payer_at_dx
[factor] |
1. Private_managed.20
2. Not_insured.1
3. Not_insured_self_pay.2
4. Insurance.10
5. Private_FFS.21
6. Medicaid.31
7. Medicaid_managed_care.35
8. Medicare_medicare.60
9. Medicare_suppl.61
10. Medicare_managed_care.62
11. Medicare_private_suppl.63
12. Medicare_medicaid.64
13. TRICARE.65
14. Military.66
15. Veterans_Affairs.67
16. Indian_PHS.68
17. Unknown.99 |
| 359 | ( | 33.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 2.2% | ) | | 32 | ( | 3.0% | ) | | 34 | ( | 3.2% | ) | | 10 | ( | 0.9% | ) | | 107 | ( | 10.1% | ) | | 62 | ( | 5.8% | ) | | 91 | ( | 8.6% | ) | | 80 | ( | 7.5% | ) | | 27 | ( | 2.5% | ) | | 38 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 99 | ( | 9.3% | ) | | 0 | ( | 0.0% | ) | | 100 | ( | 9.4% | ) |
|
 |
25
(2.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
primary_payer_at_dx
[factor] |
1. Private_managed.20
2. Not_insured.1
3. Not_insured_self_pay.2
4. Insurance.10
5. Private_FFS.21
6. Medicaid.31
7. Medicaid_managed_care.35
8. Medicare_medicare.60
9. Medicare_suppl.61
10. Medicare_managed_care.62
11. Medicare_private_suppl.63
12. Medicare_medicaid.64
13. TRICARE.65
14. Military.66
15. Veterans_Affairs.67
16. Indian_PHS.68
17. Unknown.99 |
| 10 | ( | 66.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.7% | ) | | 1 | ( | 6.7% | ) | | 2 | ( | 13.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1
(6.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SEER SUMMARY STAGE 2000
Description: Code for summary stage at the initial diagnosis or treatment of the reportable tumor. For hospital registries, CoC requires its use in the absence of a defined AJCC classification. For site-specific definitions of categories, see SEER Summary Staging Manual 2000.
Summary stage should include all information available through completion of surgery(ies) in the first course of treatment or within 4 months of diagnosis in the absence of disease progression, whichever is longer.
Rationale: Stage information is important when evaluating the effects of cancer control programs. It is crucial in understanding whether changes over time in incidence rates or outcomes are due to earlier detection of the cancers. In addition, cancer treatment cannot be studied without knowing the stage at diagnosis.
Codes
- 0 In situ
- 1 Localized
- 2 Regional, direct extension only
- 3 Regional, regional lymph nodes only
- 4 Regional, direct extension and regional lymph nodes
- 5 Regional, NOS
- 7 Distant
- 8 Not applicable
- 9 Unstaged
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#759
All data
st_css() #IMPORTANT!
seersummarystage2000 <- as.factor(trimws(d[,"seersummarystage2000"]))
levels(seersummarystage2000)[levels(seersummarystage2000)=="1"] <- "Localized.1"
levels(seersummarystage2000)[levels(seersummarystage2000)=="2"] <- "Regional_direct_extension.2"
levels(seersummarystage2000)[levels(seersummarystage2000)=="3"] <- "Regional_lymph_nodes.3"
levels(seersummarystage2000)[levels(seersummarystage2000)=="4"] <- "Regional_both_23.4"
levels(seersummarystage2000)[levels(seersummarystage2000)=="5"] <- "Regional_NOS.5"
levels(seersummarystage2000)[levels(seersummarystage2000)=="7"] <- "Distant.7"
levels(seersummarystage2000)[levels(seersummarystage2000)=="9"] <- "Unstaged.9"
new.d <- data.frame(new.d, seersummarystage2000)
new.d <- apply_labels(new.d, seersummarystage2000 = "seer_summary_stage_2000")
temp.d <- data.frame (new.d.1, seersummarystage2000)
summarytools::view(dfSummary(new.d$seersummarystage2000, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seersummarystage2000
[labelled, factor] |
seer_summary_stage_2000 |
1. Localized.1
2. Regional_direct_extension
3. Regional_lymph_nodes.3
4. Regional_both_23.4
5. Regional_NOS.5
6. Distant.7
7. Unstaged.9 |
| 1652 | ( | 78.0% | ) | | 284 | ( | 13.4% | ) | | 30 | ( | 1.4% | ) | | 48 | ( | 2.3% | ) | | 2 | ( | 0.1% | ) | | 56 | ( | 2.6% | ) | | 46 | ( | 2.2% | ) |
|
 |
49
(2.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_2000
[factor] |
1. Localized.1
2. Regional_direct_extension
3. Regional_lymph_nodes.3
4. Regional_both_23.4
5. Regional_NOS.5
6. Distant.7
7. Unstaged.9 |
| 142 | ( | 75.1% | ) | | 28 | ( | 14.8% | ) | | 1 | ( | 0.5% | ) | | 7 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.6% | ) | | 6 | ( | 3.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_2000
[factor] |
1. Localized.1
2. Regional_direct_extension
3. Regional_lymph_nodes.3
4. Regional_both_23.4
5. Regional_NOS.5
6. Distant.7
7. Unstaged.9 |
| 140 | ( | 80.5% | ) | | 24 | ( | 13.8% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.4% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_2000
[factor] |
1. Localized.1
2. Regional_direct_extension
3. Regional_lymph_nodes.3
4. Regional_both_23.4
5. Regional_NOS.5
6. Distant.7
7. Unstaged.9 |
| 170 | ( | 71.7% | ) | | 36 | ( | 15.2% | ) | | 7 | ( | 3.0% | ) | | 11 | ( | 4.6% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 3.8% | ) | | 4 | ( | 1.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_2000
[factor] |
1. Localized.1
2. Regional_direct_extension
3. Regional_lymph_nodes.3
4. Regional_both_23.4
5. Regional_NOS.5
6. Distant.7
7. Unstaged.9 |
| 99 | ( | 69.7% | ) | | 24 | ( | 16.9% | ) | | 1 | ( | 0.7% | ) | | 8 | ( | 5.6% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 4.2% | ) | | 4 | ( | 2.8% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_2000
[factor] |
1. Localized.1
2. Regional_direct_extension
3. Regional_lymph_nodes.3
4. Regional_both_23.4
5. Regional_NOS.5
6. Distant.7
7. Unstaged.9 |
| 206 | ( | 71.0% | ) | | 65 | ( | 22.4% | ) | | 5 | ( | 1.7% | ) | | 5 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.4% | ) | | 5 | ( | 1.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_2000
[factor] |
1. Localized.1
2. Regional_direct_extension
3. Regional_lymph_nodes.3
4. Regional_both_23.4
5. Regional_NOS.5
6. Distant.7
7. Unstaged.9 |
| 894 | ( | 82.4% | ) | | 107 | ( | 9.9% | ) | | 14 | ( | 1.3% | ) | | 15 | ( | 1.4% | ) | | 2 | ( | 0.2% | ) | | 26 | ( | 2.4% | ) | | 27 | ( | 2.5% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_2000
[factor] |
1. Localized.1
2. Regional_direct_extension
3. Regional_lymph_nodes.3
4. Regional_both_23.4
5. Regional_NOS.5
6. Distant.7
7. Unstaged.9 |
| 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
15
(93.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SEER SUMMARY STAGE 1977
Description: Code for summary stage at the initial diagnosis or treatment of the reportable tumor. This has traditionally been used by central registries to monitor time trends. For hospital registries, CoC requires its use in the absence of a defined AJCC classification. For site-specific definitions of categories, see the SEER Summary Staging Guide.
SEER Summary Stage 1977 is limited to information available within 2 months of the date of diagnosis. NAACCR approved extension of this time period to 4 months for prostate tumors diagnosed beginning January 1, 1995.
Rationale: Stage information is important when evaluating the effects of cancer control programs. It is crucial for understanding whether changes over time in incidence rates or outcomes are due to earlier detection of the cancers. In addition, cancer treatment cannot be studied without knowing the stage at diagnosis.
To study historical trends in stage, the coding system must be relatively unchanged (stable) over time. AJCC’s TNM system is updated periodically to maintain clinical relevance with changes in diagnosis and treatment. The surveillance registries often rely on the Summary Stage, which they consider to be more “stable.” Summary Stage has been in widespread use, either as the primary staging scheme or a secondary scheme, in most central and hospital registries since 1977.
Codes
- 9 Unstaged
- 0 In situ
- 1 Localized
- 2 Regional, direct extension only
- 3 Regional, regional lymph nodes only
- 4 Regional, direct extension and regional lymph nodes
- 5 Regional, NOS
- 7 Distant
- 8 Not applicable
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#760
All data
st_css() #IMPORTANT!
seersummarystage1977 <- as.factor(trimws(d[,"seersummarystage1977"]))
levels(seersummarystage1977)[levels(seersummarystage1977)=="1"] <- "Localized.1"
levels(seersummarystage1977)[levels(seersummarystage1977)=="2"] <- "Regional_direct_extension.2"
levels(seersummarystage1977)[levels(seersummarystage1977)=="4"] <- "Regional_both.4"
levels(seersummarystage1977)[levels(seersummarystage1977)=="9"] <- "Unstaged.9"
new.d <- data.frame(new.d, seersummarystage1977)
new.d <- apply_labels(new.d, seersummarystage1977 = "seer_summary_stage_1977")
temp.d <- data.frame (new.d.1, seersummarystage1977)
summarytools::view(dfSummary(new.d$seersummarystage1977, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seersummarystage1977
[labelled, factor] |
seer_summary_stage_1977 |
1. Localized.1
2. Regional_direct_extension
3. Regional_both.4
4. Unstaged.9 |
| 11 | ( | 4.6% | ) | | 4 | ( | 1.7% | ) | | 1 | ( | 0.4% | ) | | 221 | ( | 93.2% | ) |
|
 |
1930
(89.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_1977
[factor] |
1. Localized.1
2. Regional_direct_extension.2
3. Regional_both.4
4. Unstaged.9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_1977
[factor] |
1. Localized.1
2. Regional_direct_extension.2
3. Regional_both.4
4. Unstaged.9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_1977
[factor] |
1. Localized.1
2. Regional_direct_extension
3. Regional_both.4
4. Unstaged.9 |
| 11 | ( | 4.6% | ) | | 4 | ( | 1.7% | ) | | 1 | ( | 0.4% | ) | | 221 | ( | 93.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_1977
[factor] |
1. Localized.1
2. Regional_direct_extension.2
3. Regional_both.4
4. Unstaged.9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_1977
[factor] |
1. Localized.1
2. Regional_direct_extension.2
3. Regional_both.4
4. Unstaged.9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_1977
[factor] |
1. Localized.1
2. Regional_direct_extension.2
3. Regional_both.4
4. Unstaged.9 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
seer_summary_stage_1977
[factor] |
1. Localized.1
2. Regional_direct_extension.2
3. Regional_both.4
4. Unstaged.9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SUMMARY STAGE 2018
Description: Derived Summary Stage 2018 is derived using the EOD data collection system (EOD Primary Tumor [772], EOD Regional Nodes [774] and EOD Mets [776]) algorithm. Other data items may be included in the derivation process. Effective for cases diagnosed 1/1/2018+.
Rationale: The SEER program has collected staging information on cases since its inception in 1973. Summary Stage groups cases into broad categories of in situ, local, regional, and distant. Summary Stage can be used to evaluate disease spread at diagnosis, treatment patterns and outcomes over time.
Derived Summary Stage 2018 [762] is only available at the central registry level. Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
Codes
- 0 In situ
- 1 Localized
- 2 Regional, direct extension only
- 3 Regional, regional lymph nodes only
- 4 Regional, direct extension and regional lymph nodes
- 7 Distant
- 8 Benign, borderline
- 9 Unknown if extension or metastasis (unstaged, unknown, or unspecified)/Death certificate only case
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#762
All data
derivedsummarystage2018 <- as.factor(d[,"derivedsummarystage2018"])
levels(derivedsummarystage2018) <- list(In_situ.0="0",
Localized.1="1",
Regional_direct.2="2",
Regional_regional.3="3",
Regional_direct_regional.4="4",
Distant.7="7",
Benign_borderline.8="8",
Unknown.9="9")
derivedsummarystage2018 <- relevel(derivedsummarystage2018, ref="Localized.1")
new.d <- data.frame(new.d, derivedsummarystage2018)
new.d <- apply_labels(new.d, derivedsummarystage2018 = "Tumor Staging")
#summary(new.d$derivedsummarystage2018)
temp.d <- data.frame (new.d.1, derivedsummarystage2018)
summarytools::view(dfSummary(new.d$derivedsummarystage2018, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedsummarystage2018
[labelled, factor] |
Tumor Staging |
1. Localized.1
2. In_situ.0
3. Regional_direct.2
4. Regional_regional.3
5. Regional_direct_regional.
6. Distant.7
7. Benign_borderline.8
8. Unknown.9 |
| 28 | ( | 82.4% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 8.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 8.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
2133
(98.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_summary_stage_2018
[factor] |
1. Localized.1
2. In_situ.0
3. Regional_direct.2
4. Regional_regional.3
5. Regional_direct_regional.4
6. Distant.7
7. Benign_borderline.8
8. Unknown.9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_summary_stage_2018
[factor] |
1. Localized.1
2. In_situ.0
3. Regional_direct.2
4. Regional_regional.3
5. Regional_direct_regional.4
6. Distant.7
7. Benign_borderline.8
8. Unknown.9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_summary_stage_2018
[factor] |
1. Localized.1
2. In_situ.0
3. Regional_direct.2
4. Regional_regional.3
5. Regional_direct_regional.4
6. Distant.7
7. Benign_borderline.8
8. Unknown.9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_summary_stage_2018
[factor] |
1. Localized.1
2. In_situ.0
3. Regional_direct.2
4. Regional_regional.3
5. Regional_direct_regional.
6. Distant.7
7. Benign_borderline.8
8. Unknown.9 |
| 26 | ( | 81.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 9.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 9.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_summary_stage_2018
[factor] |
1. Localized.1
2. In_situ.0
3. Regional_direct.2
4. Regional_regional.3
5. Regional_direct_regional.4
6. Distant.7
7. Benign_borderline.8
8. Unknown.9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_summary_stage_2018
[factor] |
1. Localized.1
2. In_situ.0
3. Regional_direct.2
4. Regional_regional.3
5. Regional_direct_regional.
6. Distant.7
7. Benign_borderline.8
8. Unknown.9 |
| 2 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_summary_stage_2018
[factor] |
1. Localized.1
2. In_situ.0
3. Regional_direct.2
4. Regional_regional.3
5. Regional_direct_regional.4
6. Distant.7
7. Benign_borderline.8
8. Unknown.9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SUMMARY STAGE 2018
- Description: This item stores the directly assigned Summary Stage 2018. Effective for cases diagnosed 1/1/2018+.
- Rationale: The SEER program has collected staging information on cases since its inception in 1973. Summary Stage groups cases into broad categories of in situ, local, regional, and distant. Summary Stage can be used to evaluate disease spread at diagnosis, treatment patterns and outcomes over time.
- Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
- Codes
- 0 In situ
- 1 Localized only
- 2 Regional by direct extension only
- 3 Regional lymph nodes only
- 4 Regional by BOTH direct extension AND lymph node involvement
- 7 Distant site(s)/node(s) involved
- 8 Benign/borderline*
- 9 Unknown if extension or metastasis (unstaged, unknown, or unspecified) Death certificate only case
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#764
All data
st_css() #IMPORTANT!
summarystage2018 <- as.factor(trimws(d[,"summarystage2018"]))
levels(summarystage2018)[levels(summarystage2018)=="1"] <- "Localized_only.1"
levels(summarystage2018)[levels(summarystage2018)=="2"] <- "Regional_direct_extension.2"
levels(summarystage2018)[levels(summarystage2018)=="7"] <- "Distant_site_node_involved.7"
new.d <- data.frame(new.d, summarystage2018)
new.d <- apply_labels(new.d, summarystage2018 = "summary_stage_2018")
temp.d <- data.frame (new.d.1, summarystage2018)
summarytools::view(dfSummary(new.d$summarystage2018, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
summarystage2018
[labelled, factor] |
summary_stage_2018 |
1. Localized_only.1
2. Regional_direct_extension
3. Distant_site_node_involve |
|
 |
2133
(98.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
summary_stage_2018
[factor] |
1. Localized_only.1
2. Regional_direct_extension.2
3. Distant_site_node_involved.7 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
summary_stage_2018
[factor] |
1. Localized_only.1
2. Regional_direct_extension.2
3. Distant_site_node_involved.7 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
summary_stage_2018
[factor] |
1. Localized_only.1
2. Regional_direct_extension.2
3. Distant_site_node_involved.7 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
summary_stage_2018
[factor] |
1. Localized_only.1
2. Regional_direct_extension
3. Distant_site_node_involve |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
summary_stage_2018
[factor] |
1. Localized_only.1
2. Regional_direct_extension.2
3. Distant_site_node_involved.7 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
summary_stage_2018
[factor] |
1. Localized_only.1
2. Regional_direct_extension
3. Distant_site_node_involve |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
summary_stage_2018
[factor] |
1. Localized_only.1
2. Regional_direct_extension.2
3. Distant_site_node_involved.7 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
EOD PRIMARY TUMOR
- Description: EOD Primary Tumor is part of the EOD 2018 data collection system and is used to classify contiguous growth (extension) of the primary tumor within the organ of origin or its direct extension into neighboring organs. See also EOD Regional Nodes [774] and EOD Mets [776]. Effective for cases diagnosed 1/1/2018+.
- Rationale: EOD Primary Tumor is used to calculate Derived EOD 2018 T [785] (when applicable) and Derived Summary Stage 2018 [762]. Derivation will occur at the level of the central registry.
- Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
- Codes (In addition to schema-specific codes where needed)
- 000 In situ, intraepithelial, noninvasive
- 800 No evidence of primary tumor
- 999 Unknown; primary tumor not stated Primary tumor cannot be assessed Not documented in patient record Death certificate only (DCO)
- Codes
- 100 Incidental histologic finding (for example, on TURP) in 5 percent or less of tissue resected (clinically inapparent)
- 120 Tumor identified by needle biopsy (clinically inapparent/not palpable)
- 150 Incidental histologic finding (for example, on TURP), number of foci or percent of involved tissue not specified (clinically inapparent/not palpable)
- 200 Involves one-half of one side or less
- 220 Involves both lobes/sides
- 300 Localized, NOS Not known if clinically apparent or inapparent
- 350 Bladder neck, microscopic invasion
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#772
All data
st_css() #IMPORTANT!
eodprimarytumor <- as.factor(trimws(d[,"eodprimarytumor"]))
levels(eodprimarytumor)[levels(eodprimarytumor)=="100"] <- "5_percent_or_less_tissue.100"
levels(eodprimarytumor)[levels(eodprimarytumor)=="120"] <- "Identified_by_needle_biopsy.120"
levels(eodprimarytumor)[levels(eodprimarytumor)=="150"] <- "number_of_foci_not_specified.150"
levels(eodprimarytumor)[levels(eodprimarytumor)=="200"] <- "one_half.200"
levels(eodprimarytumor)[levels(eodprimarytumor)=="220"] <- "both_lobes_sides.220"
levels(eodprimarytumor)[levels(eodprimarytumor)=="300"] <- "Localized_NOS.300"
levels(eodprimarytumor)[levels(eodprimarytumor)=="350"] <- "Bladder_neck_microscopic_invasion.350"
new.d <- data.frame(new.d, eodprimarytumor)
new.d <- apply_labels(new.d, eodprimarytumor = "eod_primary_tumor")
temp.d <- data.frame (new.d.1, eodprimarytumor)
summarytools::view(dfSummary(new.d$eodprimarytumor, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eodprimarytumor
[labelled, factor] |
eod_primary_tumor |
1. 5_percent_or_less_tissue.
2. Identified_by_needle_biop
3. number_of_foci_not_specif
4. one_half.200
5. both_lobes_sides.220
6. Localized_NOS.300
7. Bladder_neck_microscopic_ |
| 1 | ( | 2.9% | ) | | 17 | ( | 50.0% | ) | | 1 | ( | 2.9% | ) | | 7 | ( | 20.6% | ) | | 1 | ( | 2.9% | ) | | 6 | ( | 17.6% | ) | | 1 | ( | 2.9% | ) |
|
 |
2133
(98.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_primary_tumor
[factor] |
1. 5_percent_or_less_tissue.100
2. Identified_by_needle_biopsy.120
3. number_of_foci_not_specified.150
4. one_half.200
5. both_lobes_sides.220
6. Localized_NOS.300
7. Bladder_neck_microscopic_invasion.350 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_primary_tumor
[factor] |
1. 5_percent_or_less_tissue.100
2. Identified_by_needle_biopsy.120
3. number_of_foci_not_specified.150
4. one_half.200
5. both_lobes_sides.220
6. Localized_NOS.300
7. Bladder_neck_microscopic_invasion.350 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_primary_tumor
[factor] |
1. 5_percent_or_less_tissue.100
2. Identified_by_needle_biopsy.120
3. number_of_foci_not_specified.150
4. one_half.200
5. both_lobes_sides.220
6. Localized_NOS.300
7. Bladder_neck_microscopic_invasion.350 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_primary_tumor
[factor] |
1. 5_percent_or_less_tissue.
2. Identified_by_needle_biop
3. number_of_foci_not_specif
4. one_half.200
5. both_lobes_sides.220
6. Localized_NOS.300
7. Bladder_neck_microscopic_ |
| 1 | ( | 3.1% | ) | | 16 | ( | 50.0% | ) | | 1 | ( | 3.1% | ) | | 6 | ( | 18.8% | ) | | 1 | ( | 3.1% | ) | | 6 | ( | 18.8% | ) | | 1 | ( | 3.1% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_primary_tumor
[factor] |
1. 5_percent_or_less_tissue.100
2. Identified_by_needle_biopsy.120
3. number_of_foci_not_specified.150
4. one_half.200
5. both_lobes_sides.220
6. Localized_NOS.300
7. Bladder_neck_microscopic_invasion.350 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_primary_tumor
[factor] |
1. 5_percent_or_less_tissue.
2. Identified_by_needle_biop
3. number_of_foci_not_specif
4. one_half.200
5. both_lobes_sides.220
6. Localized_NOS.300
7. Bladder_neck_microscopic_ |
| 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_primary_tumor
[factor] |
1. 5_percent_or_less_tissue.100
2. Identified_by_needle_biopsy.120
3. number_of_foci_not_specified.150
4. one_half.200
5. both_lobes_sides.220
6. Localized_NOS.300
7. Bladder_neck_microscopic_invasion.350 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
EOD REGIONAL NODES
- Description: EOD Regional Nodes is part of the EOD 2018 data collection system and is used to classify the regional lymph nodes involved with cancer at the time of diagnosis. See also EOD Primary Tumor [772] and EOD Mets [776]. Effective for cases diagnosed 1/1/2018+.
- Rationale: EOD Regional Nodes is used to calculate Derived EOD 2018 N [815] (when applicable) and Derived Summary Stage 2018 [762]. Derivation will occur at the level of the central registry. Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
- Codes
- 000 None
- 300 Hypogastric/Iliac, NOS/Pelvic, NOS/Pelvic, NOS/Sacral, NOS
- 800 Regional lymph node(s), NOS Lymph node(s), NOS
- 888 Not applicable–e.g., CNS, hematopoietic
- 999 Unknown
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#774
All data
st_css() #IMPORTANT!
eodregionalnodes <- as.factor(trimws(d[,"eodregionalnodes"]))
levels(eodregionalnodes)[levels(eodregionalnodes)=="000"] <- "None.100"
levels(eodregionalnodes)[levels(eodregionalnodes)=="300"] <- "Hypogastric.300"
new.d <- data.frame(new.d, eodregionalnodes)
new.d <- apply_labels(new.d, eodregionalnodes = "eod_regional_nodes")
temp.d <- data.frame (new.d.1, eodregionalnodes)
summarytools::view(dfSummary(new.d$eodregionalnodes, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eodregionalnodes
[labelled, factor] |
eod_regional_nodes |
1. None.100
2. Hypogastric.300 |
|
 |
2133
(98.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_regional_nodes
[factor] |
1. None.100
2. Hypogastric.300 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_regional_nodes
[factor] |
1. None.100
2. Hypogastric.300 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_regional_nodes
[factor] |
1. None.100
2. Hypogastric.300 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_regional_nodes
[factor] |
1. None.100
2. Hypogastric.300 |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_regional_nodes
[factor] |
1. None.100
2. Hypogastric.300 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_regional_nodes
[factor] |
1. None.100
2. Hypogastric.300 |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_regional_nodes
[factor] |
1. None.100
2. Hypogastric.300 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
EOD METS
- Description: EOD Mets is part of the EOD 2018 data collection system and is used to classify the distant site(s) of metastatic involvement at time of diagnosis. See also EOD Primary Tumor [772] and EOD Regional Nodes [774]. Effective for cases diagnosed 1/1/2018+.
- Rationale: EOD Mets is used to calculate Derived EOD 2018 M [795] (when applicable) and Derived Summary Stage 2018 [762]. Derivation will occur at the level of the central registry.
- Note: This data item was included in Standards Volume II, Version 16; however, it was not implemented until 2018.
- Codes
- 00 None No distant metastasis Unknown if distant metastasis
- 30 Bone WITH or WITHOUT distant lymph node(s)
- 50 Other specified distant metastasis/WITH or WITHOUT distant lymph node(s) or bone metastasis/Carcinomatosis
- 88 Not applicable: Information not collected for this schema Use for these sites only: HemeRetic; Ill Defined Other (includes unknown primary site); Kaposi Sarcoma; Lymphoma; Lymphoma-CLL/SLL; Myeloma Plasma Cell Disorder
- 99 Death certificate only (DCO)
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#776
All data
st_css() #IMPORTANT!
eodmets <- as.factor(trimws(d[,"eodmets"]))
levels(eodmets)[levels(eodmets)=="00"] <- "None.100"
levels(eodmets)[levels(eodmets)=="30"] <- "Bone_WITH_or_WITHOUT.30"
levels(eodmets)[levels(eodmets)=="50"] <- "Other_specified_metastasis.50"
new.d <- data.frame(new.d, eodmets)
new.d <- apply_labels(new.d, eodmets = "eod_mets")
temp.d <- data.frame (new.d.1, eodmets)
summarytools::view(dfSummary(new.d$eodmets), style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE, method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eodmets
[labelled, factor] |
eod_mets |
1. None.100
2. Bone_WITH_or_WITHOUT.30
3. Other_specified_metastasi |
|
 |
2133
(98.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_mets
[factor] |
1. None.100
2. Bone_WITH_or_WITHOUT.30
3. Other_specified_metastasis.50 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_mets
[factor] |
1. None.100
2. Bone_WITH_or_WITHOUT.30
3. Other_specified_metastasis.50 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_mets
[factor] |
1. None.100
2. Bone_WITH_or_WITHOUT.30
3. Other_specified_metastasis.50 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_mets
[factor] |
1. None.100
2. Bone_WITH_or_WITHOUT.30
3. Other_specified_metastasi |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_mets
[factor] |
1. None.100
2. Bone_WITH_or_WITHOUT.30
3. Other_specified_metastasis.50 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_mets
[factor] |
1. None.100
2. Bone_WITH_or_WITHOUT.30
3. Other_specified_metastasi |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eod_mets
[factor] |
1. None.100
2. Bone_WITH_or_WITHOUT.30
3. Other_specified_metastasis.50 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
EOD–EXTENSION
Description: Part of the 10-digit EOD [779]. Detailed site-specific codes for anatomic EOD used by SEER for tumors diagnosed from January 1, 1988, through December 31, 2003.
Codes were revised effective January 1, 1998, to reflect changes in the AJCC Cancer Staging Manual, Fifth Edition.
Rationale: Site-specific EOD codes provide extensive detail describing disease extent. The EOD codes can be grouped into different stage categories for analysis (e.g., historical summary stage categories consistent with those used in published SEER data since 1973, or more recently, AJCC stage groupings). The codes are updated as needed, but updates are usually backward compatible with old categories. See Comparative Staging Guide for Cancer6.
Codes (See SEER Extent of Disease, 1988: Codes and Coding Instructions, Third Edition8 for site-specific codes and coding rules for all EOD fields.)
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#790
All data
st_css() #IMPORTANT!
eodextension <- as.factor(trimws(d[,"eodextension"]))
new.d <- data.frame(new.d, eodextension)
new.d <- apply_labels(new.d, eodextension = "eod_extension")
temp.d <- data.frame (new.d.1, eodextension)
summarytools::view(dfSummary(new.d$eodextension), style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE, method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eodextension
[labelled, factor] |
eod_extension |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
EOD–EXTENSION PROST PATH
Description: Part of the 10-digit EOD [779]. Detailed site-specific codes for anatomic EOD used by SEER for tumors diagnosed from January 1, 1988, through December 31, 2003.
Codes were revised effective January 1, 1998, to reflect changes in the AJCC Cancer Staging Manual, Fifth Edition.
Rationale: Site-specific EOD codes provide extensive detail describing disease extent. The EOD codes can be grouped into different stage categories for analysis (e.g., historical summary stage categories consistent with those used in published SEER data since 1973, or more recently, AJCC stage groupings). The codes are updated as needed, but updates are usually backward compatible with old categories. See Comparative Staging Guide for Cancer.
EOD–Extension Prost Path is an additional field for prostate cancer only to reflect information from radical prostatectomy, effective for January 1, 1995, through December 31, 2003, diagnoses. The field is left blank for all other primaries.
Codes (See SEER Extent of Disease, 1988: Codes and Coding Instructions, Third Edition8 for site-specific codes and coding rules for all EOD fields.)
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#800
All data
st_css() #IMPORTANT!
eodextensionprostpath <- as.factor(trimws(d[,"eodextensionprostpath"]))
new.d <- data.frame(new.d, eodextensionprostpath)
new.d <- apply_labels(new.d, eodextensionprostpath = "eod_extension_prost_path")
temp.d <- data.frame (new.d.1, eodextensionprostpath)
summarytools::view(dfSummary(new.d$eodextensionprostpath), style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE, method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eodextensionprostpath
[labelled, factor] |
eod_extension_prost_path |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
EOD–LYMPH NODE INVOLV
Description: Part of the 10-digit EOD [779]. Detailed site-specific codes for anatomic EOD used by SEER for tumors diagnosed from January 1, 1988, through December 31, 2003.
Codes were revised effective January 1, 1998, to reflect changes in the AJCC Cancer Staging Manual, Fifth Edition.
Rationale: Site-specific EOD codes provide extensive detail describing disease extent. The EOD codes can be grouped into different stage categories for analysis (e.g., historical summary stage categories consistent with those used in published SEER data since 1973, or more recently, AJCC stage groupings). The codes are updated as needed, but updates are usually backward compatible with old categories. See Comparative Staging Guide for Cancer.
Codes (See SEER Extent of Disease, 1988: Codes and Coding Instructions, Third Edition8 for site-specific codes and coding rules for all EOD fields.)
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#810
All data
st_css() #IMPORTANT!
eodlymphnodeinvolv <- as.factor(trimws(d[,"eodlymphnodeinvolv"]))
new.d <- data.frame(new.d, eodlymphnodeinvolv)
new.d <- apply_labels(new.d, eodlymphnodeinvolv = "eod_lymph_node_involv")
temp.d <- data.frame (new.d.1, eodlymphnodeinvolv)
summarytools::view(dfSummary(new.d$eodlymphnodeinvolv), style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE, method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
eodlymphnodeinvolv
[labelled, factor] |
eod_lymph_node_involv |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED EOD 2018 STAGE GROUP
Description: Derived EOD 2018 Stage Group is derived using the EOD data collection system (EOD Primary Tumor [772], EOD Regional Nodes [774] and EOD Mets [776]) algorithm. Other data items may be included in the derivation process. Effective for cases diagnosed 1/1/2018+.
Rationale: Derived EOD 2018 Stage Group can be used to evaluate disease spread at diagnosis, treatment patterns and outcomes over time.
Derived EOD 2018 Stage group is only available at the central registry level.
Codes: See the most current version of EOD (https://staging.seer.cancer.gov/) for rules and site-specific codes and coding structures.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#818
All data
derivedeod2018stagegroup <- as.factor(trimws(d[,"derivedeod2018stagegroup"]))
# THIS CODING NEEDS TO BE CONFIRMED
levels(derivedeod2018stagegroup) <- list(T.1="1",
T.1B="1B",
T.2A="2A",
T.2B="2B",
T.2C="2C",
T.3="3",
T.3A="3A",
T.3B="3B",
T.3C="3C",
T.4="4",
T.4A="4A",
T.4B="4B",
Do_Not_Know.88="88",
Unknown.99="99")
derivedeod2018stagegroup <- relevel(derivedeod2018stagegroup, ref="T.1")
new.d <- data.frame(new.d, derivedeod2018stagegroup)
new.d <- apply_labels(new.d, derivedeod2018stagegroup = "Tumor Stage Group")
#summary(new.d$derivedeod2018stagegroup)
temp.d <- data.frame (new.d.1, derivedeod2018stagegroup)
summarytools::view(dfSummary(new.d$derivedeod2018stagegroup, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedeod2018stagegroup
[labelled, factor] |
Tumor Stage Group |
1. T.1
2. T.1B
3. T.2A
4. T.2B
5. T.2C
6. T.3
7. T.3A
8. T.3B
9. T.3C
10. T.4
11. T.4A
12. T.4B
13. Do_Not_Know.88
14. Unknown.99 |
| 2 | ( | 5.9% | ) | | 1 | ( | 2.9% | ) | | 2 | ( | 5.9% | ) | | 7 | ( | 20.6% | ) | | 11 | ( | 32.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.9% | ) | | 2 | ( | 5.9% | ) | | 2 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 8.8% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.9% | ) |
|
 |
2133
(98.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_eod_2018_stage_group
[factor] |
1. T.1
2. T.1B
3. T.2A
4. T.2B
5. T.2C
6. T.3
7. T.3A
8. T.3B
9. T.3C
10. T.4
11. T.4A
12. T.4B
13. Do_Not_Know.88
14. Unknown.99 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_eod_2018_stage_group
[factor] |
1. T.1
2. T.1B
3. T.2A
4. T.2B
5. T.2C
6. T.3
7. T.3A
8. T.3B
9. T.3C
10. T.4
11. T.4A
12. T.4B
13. Do_Not_Know.88
14. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_eod_2018_stage_group
[factor] |
1. T.1
2. T.1B
3. T.2A
4. T.2B
5. T.2C
6. T.3
7. T.3A
8. T.3B
9. T.3C
10. T.4
11. T.4A
12. T.4B
13. Do_Not_Know.88
14. Unknown.99 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_eod_2018_stage_group
[factor] |
1. T.1
2. T.1B
3. T.2A
4. T.2B
5. T.2C
6. T.3
7. T.3A
8. T.3B
9. T.3C
10. T.4
11. T.4A
12. T.4B
13. Do_Not_Know.88
14. Unknown.99 |
| 1 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.2% | ) | | 7 | ( | 21.9% | ) | | 11 | ( | 34.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.2% | ) | | 2 | ( | 6.2% | ) | | 2 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 9.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.2% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_eod_2018_stage_group
[factor] |
1. T.1
2. T.1B
3. T.2A
4. T.2B
5. T.2C
6. T.3
7. T.3A
8. T.3B
9. T.3C
10. T.4
11. T.4A
12. T.4B
13. Do_Not_Know.88
14. Unknown.99 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_eod_2018_stage_group
[factor] |
1. T.1
2. T.1B
3. T.2A
4. T.2B
5. T.2C
6. T.3
7. T.3A
8. T.3B
9. T.3C
10. T.4
11. T.4A
12. T.4B
13. Do_Not_Know.88
14. Unknown.99 |
| 1 | ( | 50.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_eod_2018_stage_group
[factor] |
1. T.1
2. T.1B
3. T.2A
4. T.2B
5. T.2C
6. T.3
7. T.3A
8. T.3B
9. T.3C
10. T.4
11. T.4A
12. T.4B
13. Do_Not_Know.88
14. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
REGIONAL NODES POSITIVE
- Description: Records the exact number of regional nodes examined by the pathologist and found to contain metastases. Beginning with tumors diagnosed on or after January 1, 2004, this item is a component of the Collaborative Stage system. For tumors diagnosed from 1988 through 2003, this item was part of the 10-digit EOD [779], detailed site-specific codes for anatomic EOD.
- Rationale: This data item is necessary for pathologic staging, and it serves as a quality measure for pathology reports and the extent of the surgical evaluation and treatment of the patient.
- Codes
- 00 All nodes examined are negative
- 01-89 1-89 nodes are positive (code exact number of nodes positive)
- 90 90 or more nodes are positive
- 95 Positive aspiration of lymph node(s) was performed
- 97 Positive nodes are documented, but the number is unspecified
- 98 No nodes were examined
- 99 It is unknown whether nodes are positive; not applicable; not stated in patient record
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#820
All data
st_css() #IMPORTANT!
regionalnodespositive <- as.factor(trimws(d[,"regionalnodespositive"]))
levels(regionalnodespositive)[levels(regionalnodespositive)=="0"] <- "All_negative.0"
levels(regionalnodespositive)[levels(regionalnodespositive)=="1"] <- "1_node_posi.1"
levels(regionalnodespositive)[levels(regionalnodespositive)=="2"] <- "2_nodes_posi.2"
levels(regionalnodespositive)[levels(regionalnodespositive)=="3"] <- "3_nodes_posi.3"
levels(regionalnodespositive)[levels(regionalnodespositive)=="4"] <- "4_nodes_posi.4"
levels(regionalnodespositive)[levels(regionalnodespositive)=="5"] <- "5_nodes_posi.5"
levels(regionalnodespositive)[levels(regionalnodespositive)=="6"] <- "6_nodes_posi.6"
levels(regionalnodespositive)[levels(regionalnodespositive)=="18"] <- "18_nodes_posi.18"
levels(regionalnodespositive)[levels(regionalnodespositive)=="19"] <- "19_nodes_posi.19"
levels(regionalnodespositive)[levels(regionalnodespositive)=="95"] <- "Positive_aspiration.95"
levels(regionalnodespositive)[levels(regionalnodespositive)=="98"] <- "No_nodes_examined.98"
levels(regionalnodespositive)[levels(regionalnodespositive)=="99"] <- "Unknown.99"
new.d <- data.frame(new.d, regionalnodespositive)
new.d <- apply_labels(new.d, regionalnodespositive = "regional_nodes_positive")
temp.d <- data.frame (new.d.1, regionalnodespositive)
summarytools::view(dfSummary(new.d$regionalnodespositive, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regionalnodespositive
[labelled, factor] |
regional_nodes_positive |
1. All_negative.0
2. 1_node_posi.1
3. 18_nodes_posi.18
4. 19_nodes_posi.19
5. 2_nodes_posi.2
6. 3_nodes_posi.3
7. 4_nodes_posi.4
8. 5_nodes_posi.5
9. 6_nodes_posi.6
10. Positive_aspiration.95
11. No_nodes_examined.98
12. Unknown.99 |
| 659 | ( | 30.4% | ) | | 30 | ( | 1.4% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 13 | ( | 0.6% | ) | | 5 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 1418 | ( | 65.4% | ) | | 30 | ( | 1.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_positive
[factor] |
1. All_negative.0
2. 1_node_posi.1
3. 18_nodes_posi.18
4. 19_nodes_posi.19
5. 2_nodes_posi.2
6. 3_nodes_posi.3
7. 4_nodes_posi.4
8. 5_nodes_posi.5
9. 6_nodes_posi.6
10. Positive_aspiration.95
11. No_nodes_examined.98
12. Unknown.99 |
| 59 | ( | 31.2% | ) | | 4 | ( | 2.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 122 | ( | 64.6% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_positive
[factor] |
1. All_negative.0
2. 1_node_posi.1
3. 18_nodes_posi.18
4. 19_nodes_posi.19
5. 2_nodes_posi.2
6. 3_nodes_posi.3
7. 4_nodes_posi.4
8. 5_nodes_posi.5
9. 6_nodes_posi.6
10. Positive_aspiration.95
11. No_nodes_examined.98
12. Unknown.99 |
| 35 | ( | 20.1% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 136 | ( | 78.2% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_positive
[factor] |
1. All_negative.0
2. 1_node_posi.1
3. 18_nodes_posi.18
4. 19_nodes_posi.19
5. 2_nodes_posi.2
6. 3_nodes_posi.3
7. 4_nodes_posi.4
8. 5_nodes_posi.5
9. 6_nodes_posi.6
10. Positive_aspiration.95
11. No_nodes_examined.98
12. Unknown.99 |
| 102 | ( | 43.0% | ) | | 4 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.7% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) | | 121 | ( | 51.1% | ) | | 3 | ( | 1.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_positive
[factor] |
1. All_negative.0
2. 1_node_posi.1
3. 18_nodes_posi.18
4. 19_nodes_posi.19
5. 2_nodes_posi.2
6. 3_nodes_posi.3
7. 4_nodes_posi.4
8. 5_nodes_posi.5
9. 6_nodes_posi.6
10. Positive_aspiration.95
11. No_nodes_examined.98
12. Unknown.99 |
| 62 | ( | 35.6% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 101 | ( | 58.0% | ) | | 2 | ( | 1.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_positive
[factor] |
1. All_negative.0
2. 1_node_posi.1
3. 18_nodes_posi.18
4. 19_nodes_posi.19
5. 2_nodes_posi.2
6. 3_nodes_posi.3
7. 4_nodes_posi.4
8. 5_nodes_posi.5
9. 6_nodes_posi.6
10. Positive_aspiration.95
11. No_nodes_examined.98
12. Unknown.99 |
| 159 | ( | 54.8% | ) | | 5 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 120 | ( | 41.4% | ) | | 3 | ( | 1.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_positive
[factor] |
1. All_negative.0
2. 1_node_posi.1
3. 18_nodes_posi.18
4. 19_nodes_posi.19
5. 2_nodes_posi.2
6. 3_nodes_posi.3
7. 4_nodes_posi.4
8. 5_nodes_posi.5
9. 6_nodes_posi.6
10. Positive_aspiration.95
11. No_nodes_examined.98
12. Unknown.99 |
| 237 | ( | 21.8% | ) | | 13 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 807 | ( | 74.2% | ) | | 21 | ( | 1.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_positive
[factor] |
1. All_negative.0
2. 1_node_posi.1
3. 18_nodes_posi.18
4. 19_nodes_posi.19
5. 2_nodes_posi.2
6. 3_nodes_posi.3
7. 4_nodes_posi.4
8. 5_nodes_posi.5
9. 6_nodes_posi.6
10. Positive_aspiration.95
11. No_nodes_examined.98
12. Unknown.99 |
| 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 68.8% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
REGIONAL NODES EXAMINED
- Description: Records the total number of regional lymph nodes that were removed and examined by the pathologist. Beginning with tumors diagnosed on or after January 1, 2004, this item is a component of the Collaborative Stage system.
- Rationale: This data item serves as a quality measure of the pathologic and surgical evaluation and treatment of the patient.
- Codes
- 00 No nodes were examined
- 01-89 1-89 nodes were examined (code the exact number of regional lymph nodes examined)
- 90 90 or more nodes were examined
- 95 No regional nodes were removed, but aspiration of regional nodes was performed
- 96 Regional lymph node removal was documented as a sampling, and the number of nodes is unknown/not stated
- 97 Regional lymph node removal was documented as a dissection, and the number of nodes is unknown/not stated
- 98 Regional lymph nodes were surgically removed, but the number of lymph nodes is unknown/not stated and not documented as a sampling or dissection; nodes were examined, but the number is unknown
- 99 It is unknown whether nodes were examined; not stated in patient record
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#830
All data
st_css() #IMPORTANT!
regionalnodesexamined <- as.factor(trimws(d[,"regionalnodesexamined"]))
levels(regionalnodesexamined)[levels(regionalnodesexamined)=="0"] <- "No_nodes_examined.0"
levels(regionalnodesexamined)[levels(regionalnodesexamined)=="95"] <- "No_removed_aspiration_performed.95"
levels(regionalnodesexamined)[levels(regionalnodesexamined)=="97"] <- "dissection_number_unknown.97"
levels(regionalnodesexamined)[levels(regionalnodesexamined)=="98"] <- "nodes_removed_number_unknown.98"
levels(regionalnodesexamined)[levels(regionalnodesexamined)=="99"] <- "Unknown.99"
new.d <- data.frame(new.d, regionalnodesexamined)
new.d <- apply_labels(new.d, regionalnodesexamined = "regional_nodes_examined")
temp.d <- data.frame (new.d.1, regionalnodesexamined)
summarytools::view(dfSummary(new.d$regionalnodesexamined, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regionalnodesexamined
[labelled, factor] |
regional_nodes_examined |
1. No_nodes_examined.0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 22
17. 23
18. 24
19. 25
20. 26
21. 27
22. 29
23. 3
24. 30
25. 32
26. 33
27. 34
28. 35
29. 36
30. 37
31. 39
32. 4
33. 42
34. 46
35. 5
36. 50
37. 51
38. 52
39. 6
40. 7
41. 8
42. 9
43. No_removed_aspiration_per
44. dissection_number_unknown
45. nodes_removed_number_unkn
46. Unknown.99 |
| 1418 | ( | 65.4% | ) | | 29 | ( | 1.3% | ) | | 37 | ( | 1.7% | ) | | 27 | ( | 1.2% | ) | | 20 | ( | 0.9% | ) | | 22 | ( | 1.0% | ) | | 17 | ( | 0.8% | ) | | 11 | ( | 0.5% | ) | | 15 | ( | 0.7% | ) | | 5 | ( | 0.2% | ) | | 8 | ( | 0.4% | ) | | 9 | ( | 0.4% | ) | | 92 | ( | 4.2% | ) | | 8 | ( | 0.4% | ) | | 4 | ( | 0.2% | ) | | 8 | ( | 0.4% | ) | | 5 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 67 | ( | 3.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 58 | ( | 2.7% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 73 | ( | 3.4% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 42 | ( | 1.9% | ) | | 48 | ( | 2.2% | ) | | 39 | ( | 1.8% | ) | | 33 | ( | 1.5% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 30 | ( | 1.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_examined
[factor] |
1. No_nodes_examined.0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 22
17. 23
18. 24
19. 25
20. 26
21. 27
22. 29
23. 3
24. 30
25. 32
26. 33
27. 34
28. 35
29. 36
30. 37
31. 39
32. 4
33. 42
34. 46
35. 5
36. 50
37. 51
38. 52
39. 6
40. 7
41. 8
42. 9
43. No_removed_aspiration_per
44. dissection_number_unknown
45. nodes_removed_number_unkn
46. Unknown.99 |
| 122 | ( | 64.6% | ) | | 2 | ( | 1.1% | ) | | 3 | ( | 1.6% | ) | | 6 | ( | 3.2% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 3 | ( | 1.6% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 3 | ( | 1.6% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 4.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.6% | ) | | 7 | ( | 3.7% | ) | | 1 | ( | 0.5% | ) | | 4 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_examined
[factor] |
1. No_nodes_examined.0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 22
17. 23
18. 24
19. 25
20. 26
21. 27
22. 29
23. 3
24. 30
25. 32
26. 33
27. 34
28. 35
29. 36
30. 37
31. 39
32. 4
33. 42
34. 46
35. 5
36. 50
37. 51
38. 52
39. 6
40. 7
41. 8
42. 9
43. No_removed_aspiration_per
44. dissection_number_unknown
45. nodes_removed_number_unkn
46. Unknown.99 |
| 136 | ( | 78.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_examined
[factor] |
1. No_nodes_examined.0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 22
17. 23
18. 24
19. 25
20. 26
21. 27
22. 29
23. 3
24. 30
25. 32
26. 33
27. 34
28. 35
29. 36
30. 37
31. 39
32. 4
33. 42
34. 46
35. 5
36. 50
37. 51
38. 52
39. 6
40. 7
41. 8
42. 9
43. No_removed_aspiration_per
44. dissection_number_unknown
45. nodes_removed_number_unkn
46. Unknown.99 |
| 121 | ( | 51.1% | ) | | 3 | ( | 1.3% | ) | | 7 | ( | 3.0% | ) | | 6 | ( | 2.5% | ) | | 4 | ( | 1.7% | ) | | 5 | ( | 2.1% | ) | | 4 | ( | 1.7% | ) | | 3 | ( | 1.3% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 12 | ( | 5.1% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 4.6% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 8 | ( | 3.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 9 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.1% | ) | | 4 | ( | 1.7% | ) | | 2 | ( | 0.8% | ) | | 6 | ( | 2.5% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_examined
[factor] |
1. No_nodes_examined.0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 22
17. 23
18. 24
19. 25
20. 26
21. 27
22. 29
23. 3
24. 30
25. 32
26. 33
27. 34
28. 35
29. 36
30. 37
31. 39
32. 4
33. 42
34. 46
35. 5
36. 50
37. 51
38. 52
39. 6
40. 7
41. 8
42. 9
43. No_removed_aspiration_per
44. dissection_number_unknown
45. nodes_removed_number_unkn
46. Unknown.99 |
| 101 | ( | 58.0% | ) | | 1 | ( | 0.6% | ) | | 5 | ( | 2.9% | ) | | 2 | ( | 1.1% | ) | | 3 | ( | 1.7% | ) | | 5 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 4 | ( | 2.3% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 6.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 4 | ( | 2.3% | ) | | 5 | ( | 2.9% | ) | | 7 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_examined
[factor] |
1. No_nodes_examined.0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 22
17. 23
18. 24
19. 25
20. 26
21. 27
22. 29
23. 3
24. 30
25. 32
26. 33
27. 34
28. 35
29. 36
30. 37
31. 39
32. 4
33. 42
34. 46
35. 5
36. 50
37. 51
38. 52
39. 6
40. 7
41. 8
42. 9
43. No_removed_aspiration_per
44. dissection_number_unknown
45. nodes_removed_number_unkn
46. Unknown.99 |
| 120 | ( | 41.4% | ) | | 8 | ( | 2.8% | ) | | 7 | ( | 2.4% | ) | | 3 | ( | 1.0% | ) | | 1 | ( | 0.3% | ) | | 3 | ( | 1.0% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 30 | ( | 10.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 24 | ( | 8.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 19 | ( | 6.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 18 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 14 | ( | 4.8% | ) | | 11 | ( | 3.8% | ) | | 13 | ( | 4.5% | ) | | 4 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_examined
[factor] |
1. No_nodes_examined.0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 22
17. 23
18. 24
19. 25
20. 26
21. 27
22. 29
23. 3
24. 30
25. 32
26. 33
27. 34
28. 35
29. 36
30. 37
31. 39
32. 4
33. 42
34. 46
35. 5
36. 50
37. 51
38. 52
39. 6
40. 7
41. 8
42. 9
43. No_removed_aspiration_per
44. dissection_number_unknown
45. nodes_removed_number_unkn
46. Unknown.99 |
| 807 | ( | 74.2% | ) | | 14 | ( | 1.3% | ) | | 14 | ( | 1.3% | ) | | 8 | ( | 0.7% | ) | | 7 | ( | 0.6% | ) | | 7 | ( | 0.6% | ) | | 6 | ( | 0.6% | ) | | 3 | ( | 0.3% | ) | | 9 | ( | 0.8% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 36 | ( | 3.3% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 0.4% | ) | | 20 | ( | 1.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 18 | ( | 1.7% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 2.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 15 | ( | 1.4% | ) | | 20 | ( | 1.8% | ) | | 15 | ( | 1.4% | ) | | 11 | ( | 1.0% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 21 | ( | 1.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
regional_nodes_examined
[factor] |
1. No_nodes_examined.0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 22
17. 23
18. 24
19. 25
20. 26
21. 27
22. 29
23. 3
24. 30
25. 32
26. 33
27. 34
28. 35
29. 36
30. 37
31. 39
32. 4
33. 42
34. 46
35. 5
36. 50
37. 51
38. 52
39. 6
40. 7
41. 8
42. 9
43. No_removed_aspiration_per
44. dissection_number_unknown
45. nodes_removed_number_unkn
46. Unknown.99 |
| 11 | ( | 68.8% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TNM PATH T
Description: Detailed site-specific codes for the pathologic tumor (T) as defined by AJCC and recorded by the physician.
Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.
Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.
Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#880
All data
st_css() #IMPORTANT!
tnmpatht <- as.factor(trimws(d[,"tnmpatht"]))
levels(tnmpatht)[levels(tnmpatht)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, tnmpatht)
new.d <- apply_labels(new.d, tnmpatht = "tnm_path_t")
temp.d <- data.frame (new.d.1, tnmpatht)
summarytools::view(dfSummary(new.d$tnmpatht, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnmpatht
[labelled, factor] |
tnm_path_t |
1. 1B
2. 1C
3. 2
4. 2A
5. 2B
6. 2C
7. 3A
8. 3B
9. Not_applicable.88
10. p0
11. p2
12. p2A
13. p2B
14. p2C
15. p3
16. p3A
17. p3B
18. p4
19. pX
20. X |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 15 | ( | 1.5% | ) | | 11 | ( | 1.1% | ) | | 2 | ( | 0.2% | ) | | 118 | ( | 12.0% | ) | | 24 | ( | 2.4% | ) | | 21 | ( | 2.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 23 | ( | 2.3% | ) | | 26 | ( | 2.6% | ) | | 7 | ( | 0.7% | ) | | 360 | ( | 36.6% | ) | | 3 | ( | 0.3% | ) | | 118 | ( | 12.0% | ) | | 76 | ( | 7.7% | ) | | 2 | ( | 0.2% | ) | | 103 | ( | 10.5% | ) | | 71 | ( | 7.2% | ) |
|
 |
1183
(54.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_t
[factor] |
1. 1B
2. 1C
3. 2
4. 2A
5. 2B
6. 2C
7. 3A
8. 3B
9. Not_applicable.88
10. p0
11. p2
12. p2A
13. p2B
14. p2C
15. p3
16. p3A
17. p3B
18. p4
19. pX
20. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.5% | ) | | 4 | ( | 5.9% | ) | | 2 | ( | 2.9% | ) | | 36 | ( | 52.9% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 11.8% | ) | | 9 | ( | 13.2% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 11.8% | ) | | 0 | ( | 0.0% | ) |
|
 |
121
(64.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_t
[factor] |
1. 1B
2. 1C
3. 2
4. 2A
5. 2B
6. 2C
7. 3A
8. 3B
9. Not_applicable.88
10. p0
11. p2
12. p2A
13. p2B
14. p2C
15. p3
16. p3A
17. p3B
18. p4
19. pX
20. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.8% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 7.3% | ) | | 0 | ( | 0.0% | ) | | 14 | ( | 25.5% | ) | | 1 | ( | 1.8% | ) | | 13 | ( | 23.6% | ) | | 4 | ( | 7.3% | ) | | 0 | ( | 0.0% | ) | | 18 | ( | 32.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
119
(68.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_t
[factor] |
1. 1B
2. 1C
3. 2
4. 2A
5. 2B
6. 2C
7. 3A
8. 3B
9. Not_applicable.88
10. p0
11. p2
12. p2A
13. p2B
14. p2C
15. p3
16. p3A
17. p3B
18. p4
19. pX
20. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.0% | ) | | 2 | ( | 2.0% | ) | | 0 | ( | 0.0% | ) | | 65 | ( | 64.4% | ) | | 1 | ( | 1.0% | ) | | 19 | ( | 18.8% | ) | | 6 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) |
|
 |
136
(57.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_t
[factor] |
1. 1B
2. 1C
3. 2
4. 2A
5. 2B
6. 2C
7. 3A
8. 3B
9. Not_applicable.88
10. p0
11. p2
12. p2A
13. p2B
14. p2C
15. p3
16. p3A
17. p3B
18. p4
19. pX
20. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.8% | ) | | 1 | ( | 1.4% | ) | | 16 | ( | 22.2% | ) | | 4 | ( | 5.6% | ) | | 2 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.8% | ) | | 1 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 22 | ( | 30.6% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 15.3% | ) | | 9 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) |
|
 |
102
(58.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_t
[factor] |
1. 1B
2. 1C
3. 2
4. 2A
5. 2B
6. 2C
7. 3A
8. 3B
9. Not_applicable.88
10. p0
11. p2
12. p2A
13. p2B
14. p2C
15. p3
16. p3A
17. p3B
18. p4
19. pX
20. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 4 | ( | 2.1% | ) | | 1 | ( | 0.5% | ) | | 37 | ( | 19.6% | ) | | 9 | ( | 4.8% | ) | | 6 | ( | 3.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 4 | ( | 2.1% | ) | | 1 | ( | 0.5% | ) | | 62 | ( | 32.8% | ) | | 1 | ( | 0.5% | ) | | 30 | ( | 15.9% | ) | | 15 | ( | 7.9% | ) | | 1 | ( | 0.5% | ) | | 6 | ( | 3.2% | ) | | 10 | ( | 5.3% | ) |
|
 |
101
(34.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_t
[factor] |
1. 1B
2. 1C
3. 2
4. 2A
5. 2B
6. 2C
7. 3A
8. 3B
9. Not_applicable.88
10. p0
11. p2
12. p2A
13. p2B
14. p2C
15. p3
16. p3A
17. p3B
18. p4
19. pX
20. X |
| 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 14 | ( | 2.9% | ) | | 5 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 61 | ( | 12.4% | ) | | 10 | ( | 2.0% | ) | | 13 | ( | 2.6% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 17 | ( | 3.5% | ) | | 11 | ( | 2.2% | ) | | 4 | ( | 0.8% | ) | | 161 | ( | 32.8% | ) | | 0 | ( | 0.0% | ) | | 37 | ( | 7.5% | ) | | 33 | ( | 6.7% | ) | | 1 | ( | 0.2% | ) | | 64 | ( | 13.0% | ) | | 57 | ( | 11.6% | ) |
|
 |
596
(54.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_t
[factor] |
1. 1B
2. 1C
3. 2
4. 2A
5. 2B
6. 2C
7. 3A
8. 3B
9. Not_applicable.88
10. p0
11. p2
12. p2A
13. p2B
14. p2C
15. p3
16. p3A
17. p3B
18. p4
19. pX
20. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 50.0% | ) | | 1 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 37.5% | ) |
|
 |
8
(50.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TNM PATH N
Description: Detailed site-specific codes for the pathologic nodes (N) as defined by AJCC and recorded by physician.
Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.
Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.
Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#890
All data
st_css() #IMPORTANT!
tnmpathn <- as.factor(trimws(d[,"tnmpathn"]))
levels(tnmpathn)[levels(tnmpathn)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, tnmpathn)
new.d <- apply_labels(new.d, tnmpathn = "tnm_path_n")
temp.d <- data.frame (new.d.1, tnmpathn)
summarytools::view(dfSummary(new.d$tnmpathn, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnmpathn
[labelled, factor] |
tnm_path_n |
1. 0
2. 1
3. Not_applicable.88
4. c0
5. p0
6. p1
7. pX
8. X |
| 154 | ( | 19.6% | ) | | 9 | ( | 1.1% | ) | | 1 | ( | 0.1% | ) | | 71 | ( | 9.0% | ) | | 285 | ( | 36.2% | ) | | 27 | ( | 3.4% | ) | | 163 | ( | 20.7% | ) | | 77 | ( | 9.8% | ) |
|
 |
1380
(63.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_n
[factor] |
1. 0
2. 1
3. Not_applicable.88
4. c0
5. p0
6. p1
7. pX
8. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.9% | ) | | 35 | ( | 51.5% | ) | | 6 | ( | 8.8% | ) | | 25 | ( | 36.8% | ) | | 0 | ( | 0.0% | ) |
|
 |
121
(64.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_n
[factor] |
1. 0
2. 1
3. Not_applicable.88
4. c0
5. p0
6. p1
7. pX
8. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 7.1% | ) | | 31 | ( | 55.4% | ) | | 2 | ( | 3.6% | ) | | 19 | ( | 33.9% | ) | | 0 | ( | 0.0% | ) |
|
 |
118
(67.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_n
[factor] |
1. 0
2. 1
3. Not_applicable.88
4. c0
5. p0
6. p1
7. pX
8. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 74 | ( | 75.5% | ) | | 4 | ( | 4.1% | ) | | 19 | ( | 19.4% | ) | | 0 | ( | 0.0% | ) |
|
 |
139
(58.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_n
[factor] |
1. 0
2. 1
3. Not_applicable.88
4. c0
5. p0
6. p1
7. pX
8. X |
| 24 | ( | 96.0% | ) | | 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
149
(85.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_n
[factor] |
1. 0
2. 1
3. Not_applicable.88
4. c0
5. p0
6. p1
7. pX
8. X |
| 52 | ( | 94.5% | ) | | 3 | ( | 5.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
235
(81.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_n
[factor] |
1. 0
2. 1
3. Not_applicable.88
4. c0
5. p0
6. p1
7. pX
8. X |
| 73 | ( | 15.2% | ) | | 5 | ( | 1.0% | ) | | 1 | ( | 0.2% | ) | | 64 | ( | 13.3% | ) | | 145 | ( | 30.2% | ) | | 15 | ( | 3.1% | ) | | 100 | ( | 20.8% | ) | | 77 | ( | 16.0% | ) |
|
 |
607
(55.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_n
[factor] |
1. 0
2. 1
3. Not_applicable.88
4. c0
5. p0
6. p1
7. pX
8. X |
| 5 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
11
(68.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TNM PATH M
Description: Detailed site-specific codes for the pathologic metastases (M) as defined by AJCC and recorded by the physician.
Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.
Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.
Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#900
All data
st_css() #IMPORTANT!
tnmpathm <- as.factor(trimws(d[,"tnmpathm"]))
levels(tnmpathm)[levels(tnmpathm)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, tnmpathm)
new.d <- apply_labels(new.d, tnmpathm = "tnm_path_m")
temp.d <- data.frame (new.d.1, tnmpathm)
summarytools::view(dfSummary(new.d$tnmpathm, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnmpathm
[labelled, factor] |
tnm_path_m |
1. 0
2. Not_applicable.88
3. c0
4. c1
5. c1A
6. c1B
7. p1A
8. p1B
9. p1C |
| 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 562 | ( | 98.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) |
|
 |
1596
(73.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_m
[factor] |
1. 0
2. Not_applicable.88
3. c0
4. c1
5. c1A
6. c1B
7. p1A
8. p1B
9. p1C |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 42 | ( | 97.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
146
(77.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_m
[factor] |
1. 0
2. Not_applicable.88
3. c0
4. c1
5. c1A
6. c1B
7. p1A
8. p1B
9. p1C |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 33 | ( | 97.1% | ) | | 1 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
140
(80.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_m
[factor] |
1. 0
2. Not_applicable.88
3. c0
4. c1
5. c1A
6. c1B
7. p1A
8. p1B
9. p1C |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 67 | ( | 98.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.5% | ) |
|
 |
169
(71.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_m
[factor] |
1. 0
2. Not_applicable.88
3. c0
4. c1
5. c1A
6. c1B
7. p1A
8. p1B
9. p1C |
| 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 41 | ( | 95.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) |
|
 |
131
(75.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_m
[factor] |
1. 0
2. Not_applicable.88
3. c0
4. c1
5. c1A
6. c1B
7. p1A
8. p1B
9. p1C |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 116 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
174
(60.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_m
[factor] |
1. 0
2. Not_applicable.88
3. c0
4. c1
5. c1A
6. c1B
7. p1A
8. p1B
9. p1C |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 263 | ( | 98.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
820
(75.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_m
[factor] |
1. 0
2. Not_applicable.88
3. c0
4. c1
5. c1A
6. c1B
7. p1A
8. p1B
9. p1C |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TNM PATH STAGE GROUP
Description: Detailed site-specific codes for the pathologic stage group as defined by AJCC and recorded by the physician.
Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.
Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- 99 Unknown, unstaged
Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#910
All data
st_css() #IMPORTANT!
tnmpathstagegroup <- as.factor(trimws(d[,"tnmpathstagegroup"]))
levels(tnmpathstagegroup)[levels(tnmpathstagegroup)=="99"] <- "Unknown.99"
new.d <- data.frame(new.d, tnmpathstagegroup)
new.d <- apply_labels(new.d, tnmpathstagegroup = "tnm_path_stage_group")
temp.d <- data.frame (new.d.1, tnmpathstagegroup)
summarytools::view(dfSummary(new.d$tnmpathstagegroup, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnmpathstagegroup
[labelled, factor] |
tnm_path_stage_group |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 10 | ( | 0.8% | ) | | 6 | ( | 0.5% | ) | | 5 | ( | 0.4% | ) | | 36 | ( | 2.7% | ) | | 182 | ( | 13.8% | ) | | 55 | ( | 4.2% | ) | | 1027 | ( | 77.7% | ) |
|
 |
846
(39.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 7.7% | ) | | 7 | ( | 6.7% | ) | | 87 | ( | 83.7% | ) |
|
 |
85
(45.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 15 | ( | 13.9% | ) | | 3 | ( | 2.8% | ) | | 90 | ( | 83.3% | ) |
|
 |
66
(37.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 21 | ( | 22.1% | ) | | 5 | ( | 5.3% | ) | | 68 | ( | 71.6% | ) |
|
 |
142
(59.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 4.0% | ) | | 32 | ( | 25.4% | ) | | 18 | ( | 14.3% | ) | | 9 | ( | 7.1% | ) | | 62 | ( | 49.2% | ) |
|
 |
48
(27.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 2 | ( | 1.2% | ) | | 3 | ( | 1.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 54 | ( | 32.5% | ) | | 8 | ( | 4.8% | ) | | 99 | ( | 59.6% | ) |
|
 |
124
(42.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 6 | ( | 0.8% | ) | | 2 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 65 | ( | 9.1% | ) | | 23 | ( | 3.2% | ) | | 617 | ( | 86.5% | ) |
|
 |
374
(34.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_path_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 44.4% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 44.4% | ) |
|
 |
7
(43.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TNM CLIN T
Description: Detailed site-specific codes for the clinical tumor (T) as defined by AJCC and recorded by the physician.
Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.
Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.
Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#940
All data
st_css() #IMPORTANT!
tnmclint <- as.factor(trimws(d[,"tnmclint"]))
levels(tnmclint)[levels(tnmclint)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, tnmclint)
new.d <- apply_labels(new.d, tnmclint = "tnm_clin_t")
temp.d <- data.frame (new.d.1, tnmclint)
summarytools::view(dfSummary(new.d$tnmclint, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnmclint
[labelled, factor] |
tnm_clin_t |
1. 0
2. 1
3. 1A
4. 1B
5. 1C
6. 2
7. 2A
8. 2B
9. 2C
10. 3
11. 3A
12. 3B
13. 4
14. c1
15. c1A
16. c1B
17. c1C
18. c2
19. c2A
20. c2B
21. c2C
22. c3
23. c3A
24. c3B
25. c4
26. cX
27. X |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 316 | ( | 17.4% | ) | | 24 | ( | 1.3% | ) | | 15 | ( | 0.8% | ) | | 9 | ( | 0.5% | ) | | 18 | ( | 1.0% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 5 | ( | 0.3% | ) | | 5 | ( | 0.3% | ) | | 22 | ( | 1.2% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 1028 | ( | 56.5% | ) | | 59 | ( | 3.2% | ) | | 76 | ( | 4.2% | ) | | 46 | ( | 2.5% | ) | | 82 | ( | 4.5% | ) | | 7 | ( | 0.4% | ) | | 19 | ( | 1.0% | ) | | 24 | ( | 1.3% | ) | | 5 | ( | 0.3% | ) | | 28 | ( | 1.5% | ) | | 13 | ( | 0.7% | ) |
|
 |
348
(16.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_t
[factor] |
1. 0
2. 1
3. 1A
4. 1B
5. 1C
6. 2
7. 2A
8. 2B
9. 2C
10. 3
11. 3A
12. 3B
13. 4
14. c1
15. c1A
16. c1B
17. c1C
18. c2
19. c2A
20. c2B
21. c2C
22. c3
23. c3A
24. c3B
25. c4
26. cX
27. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.8% | ) | | 1 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 87 | ( | 70.2% | ) | | 6 | ( | 4.8% | ) | | 7 | ( | 5.6% | ) | | 4 | ( | 3.2% | ) | | 10 | ( | 8.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.4% | ) | | 1 | ( | 0.8% | ) | | 4 | ( | 3.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
65
(34.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_t
[factor] |
1. 0
2. 1
3. 1A
4. 1B
5. 1C
6. 2
7. 2A
8. 2B
9. 2C
10. 3
11. 3A
12. 3B
13. 4
14. c1
15. c1A
16. c1B
17. c1C
18. c2
19. c2A
20. c2B
21. c2C
22. c3
23. c3A
24. c3B
25. c4
26. cX
27. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 97 | ( | 68.8% | ) | | 2 | ( | 1.4% | ) | | 17 | ( | 12.1% | ) | | 6 | ( | 4.3% | ) | | 13 | ( | 9.2% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 2 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) |
|
 |
33
(19.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_t
[factor] |
1. 0
2. 1
3. 1A
4. 1B
5. 1C
6. 2
7. 2A
8. 2B
9. 2C
10. 3
11. 3A
12. 3B
13. 4
14. c1
15. c1A
16. c1B
17. c1C
18. c2
19. c2A
20. c2B
21. c2C
22. c3
23. c3A
24. c3B
25. c4
26. cX
27. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.2% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 105 | ( | 64.8% | ) | | 9 | ( | 5.6% | ) | | 13 | ( | 8.0% | ) | | 5 | ( | 3.1% | ) | | 16 | ( | 9.9% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.2% | ) | | 3 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
75
(31.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_t
[factor] |
1. 0
2. 1
3. 1A
4. 1B
5. 1C
6. 2
7. 2A
8. 2B
9. 2C
10. 3
11. 3A
12. 3B
13. 4
14. c1
15. c1A
16. c1B
17. c1C
18. c2
19. c2A
20. c2B
21. c2C
22. c3
23. c3A
24. c3B
25. c4
26. cX
27. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 24 | ( | 18.2% | ) | | 5 | ( | 3.8% | ) | | 1 | ( | 0.8% | ) | | 1 | ( | 0.8% | ) | | 2 | ( | 1.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 56 | ( | 42.4% | ) | | 10 | ( | 7.6% | ) | | 4 | ( | 3.0% | ) | | 10 | ( | 7.6% | ) | | 7 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.5% | ) | | 1 | ( | 0.8% | ) | | 1 | ( | 0.8% | ) | | 4 | ( | 3.0% | ) | | 1 | ( | 0.8% | ) |
|
 |
42
(24.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_t
[factor] |
1. 0
2. 1
3. 1A
4. 1B
5. 1C
6. 2
7. 2A
8. 2B
9. 2C
10. 3
11. 3A
12. 3B
13. 4
14. c1
15. c1A
16. c1B
17. c1C
18. c2
19. c2A
20. c2B
21. c2C
22. c3
23. c3A
24. c3B
25. c4
26. cX
27. X |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 73 | ( | 25.9% | ) | | 10 | ( | 3.5% | ) | | 3 | ( | 1.1% | ) | | 2 | ( | 0.7% | ) | | 3 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 4 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 126 | ( | 44.7% | ) | | 16 | ( | 5.7% | ) | | 10 | ( | 3.5% | ) | | 5 | ( | 1.8% | ) | | 5 | ( | 1.8% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) | | 5 | ( | 1.8% | ) | | 1 | ( | 0.4% | ) | | 7 | ( | 2.5% | ) | | 2 | ( | 0.7% | ) |
|
 |
8
(2.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_t
[factor] |
1. 0
2. 1
3. 1A
4. 1B
5. 1C
6. 2
7. 2A
8. 2B
9. 2C
10. 3
11. 3A
12. 3B
13. 4
14. c1
15. c1A
16. c1B
17. c1C
18. c2
19. c2A
20. c2B
21. c2C
22. c3
23. c3A
24. c3B
25. c4
26. cX
27. X |
| 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 211 | ( | 21.8% | ) | | 9 | ( | 0.9% | ) | | 11 | ( | 1.1% | ) | | 6 | ( | 0.6% | ) | | 13 | ( | 1.3% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 5 | ( | 0.5% | ) | | 4 | ( | 0.4% | ) | | 13 | ( | 1.3% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 556 | ( | 57.6% | ) | | 16 | ( | 1.7% | ) | | 25 | ( | 2.6% | ) | | 16 | ( | 1.7% | ) | | 31 | ( | 3.2% | ) | | 5 | ( | 0.5% | ) | | 8 | ( | 0.8% | ) | | 12 | ( | 1.2% | ) | | 1 | ( | 0.1% | ) | | 7 | ( | 0.7% | ) | | 7 | ( | 0.7% | ) |
|
 |
121
(11.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_t
[factor] |
1. 0
2. 1
3. 1A
4. 1B
5. 1C
6. 2
7. 2A
8. 2B
9. 2C
10. 3
11. 3A
12. 3B
13. 4
14. c1
15. c1A
16. c1B
17. c1C
18. c2
19. c2A
20. c2B
21. c2C
22. c3
23. c3A
24. c3B
25. c4
26. cX
27. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 66.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 8.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 25.0% | ) |
|
 |
4
(25.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TNM CLIN N
Description: Detailed site-specific codes for the clinical nodes (N) as defined by AJCC and recorded by the physician.
Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.
Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.
Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#950
All data
st_css() #IMPORTANT!
tnmclinn <- as.factor(trimws(d[,"tnmclinn"]))
levels(tnmclinn)[levels(tnmclinn)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, tnmclinn)
new.d <- apply_labels(new.d, tnmclinn = "tnm_clin_n")
temp.d <- data.frame (new.d.1, tnmclinn)
summarytools::view(dfSummary(new.d$tnmclinn, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnmclinn
[labelled, factor] |
tnm_clin_n |
1. 0
2. 1
3. c0
4. c1
5. cX
6. X |
| 396 | ( | 26.0% | ) | | 8 | ( | 0.5% | ) | | 1050 | ( | 69.0% | ) | | 32 | ( | 2.1% | ) | | 23 | ( | 1.5% | ) | | 12 | ( | 0.8% | ) |
|
 |
646
(29.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_n
[factor] |
1. 0
2. 1
3. c0
4. c1
5. cX
6. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 116 | ( | 93.5% | ) | | 3 | ( | 2.4% | ) | | 5 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
65
(34.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_n
[factor] |
1. 0
2. 1
3. c0
4. c1
5. cX
6. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 135 | ( | 95.7% | ) | | 5 | ( | 3.5% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
33
(19.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_n
[factor] |
1. 0
2. 1
3. c0
4. c1
5. cX
6. X |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 144 | ( | 90.0% | ) | | 12 | ( | 7.5% | ) | | 4 | ( | 2.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
77
(32.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_n
[factor] |
1. 0
2. 1
3. c0
4. c1
5. cX
6. X |
| 34 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
140
(80.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_n
[factor] |
1. 0
2. 1
3. c0
4. c1
5. cX
6. X |
| 96 | ( | 99.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
193
(66.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_n
[factor] |
1. 0
2. 1
3. c0
4. c1
5. cX
6. X |
| 258 | ( | 27.0% | ) | | 7 | ( | 0.7% | ) | | 655 | ( | 68.4% | ) | | 12 | ( | 1.3% | ) | | 13 | ( | 1.4% | ) | | 12 | ( | 1.3% | ) |
|
 |
130
(12.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_n
[factor] |
1. 0
2. 1
3. c0
4. c1
5. cX
6. X |
| 8 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
8
(50.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TNM CLIN M
Description: Detailed site-specific codes for the clinical metastases (M) as defined by AJCC and recorded by the physician.
Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.
Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual. This field is left blank if no information at all is available to code this item.
Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#960
All data
st_css() #IMPORTANT!
tnmclinm <- as.factor(trimws(d[,"tnmclinm"]))
levels(tnmclinm)[levels(tnmclinm)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, tnmclinm)
new.d <- apply_labels(new.d, tnmclinm = "tnm_clin_m")
temp.d <- data.frame (new.d.1, tnmclinm)
summarytools::view(dfSummary(new.d$tnmclinm, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnmclinm
[labelled, factor] |
tnm_clin_m |
1. 0
2. 1
3. 1B
4. 1C
5. Not_applicable.88
6. c0
7. c1
8. c1A
9. c1B
10. c1C
11. p1A
12. p1B |
| 385 | ( | 25.8% | ) | | 2 | ( | 0.1% | ) | | 5 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1061 | ( | 71.1% | ) | | 9 | ( | 0.6% | ) | | 3 | ( | 0.2% | ) | | 20 | ( | 1.3% | ) | | 3 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) |
|
 |
674
(31.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_m
[factor] |
1. 0
2. 1
3. 1B
4. 1C
5. Not_applicable.88
6. c0
7. c1
8. c1A
9. c1B
10. c1C
11. p1A
12. p1B |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 118 | ( | 96.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.8% | ) | | 3 | ( | 2.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
67
(35.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_m
[factor] |
1. 0
2. 1
3. 1B
4. 1C
5. Not_applicable.88
6. c0
7. c1
8. c1A
9. c1B
10. c1C
11. p1A
12. p1B |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 132 | ( | 95.7% | ) | | 2 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.2% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
36
(20.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_m
[factor] |
1. 0
2. 1
3. 1B
4. 1C
5. Not_applicable.88
6. c0
7. c1
8. c1A
9. c1B
10. c1C
11. p1A
12. p1B |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 149 | ( | 94.3% | ) | | 3 | ( | 1.9% | ) | | 1 | ( | 0.6% | ) | | 3 | ( | 1.9% | ) | | 2 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
79
(33.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_m
[factor] |
1. 0
2. 1
3. 1B
4. 1C
5. Not_applicable.88
6. c0
7. c1
8. c1A
9. c1B
10. c1C
11. p1A
12. p1B |
| 30 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
144
(82.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_m
[factor] |
1. 0
2. 1
3. 1B
4. 1C
5. Not_applicable.88
6. c0
7. c1
8. c1A
9. c1B
10. c1C
11. p1A
12. p1B |
| 89 | ( | 98.9% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
200
(69.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_m
[factor] |
1. 0
2. 1
3. 1B
4. 1C
5. Not_applicable.88
6. c0
7. c1
8. c1A
9. c1B
10. c1C
11. p1A
12. p1B |
| 258 | ( | 27.3% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 662 | ( | 70.0% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 11 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) |
|
 |
141
(13.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_m
[factor] |
1. 0
2. 1
3. 1B
4. 1C
5. Not_applicable.88
6. c0
7. c1
8. c1A
9. c1B
10. c1C
11. p1A
12. p1B |
| 8 | ( | 88.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
7
(43.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TNM CLIN STAGE GROUP
Description: Detailed site-specific codes for the clinical stage group as defined by AJCC and recorded by the physician.
Pathologic and clinical stage data are given three separate areas in the NAACCR Data Exchange Record Layout.
Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- 99 Unknown, not staged
Note: See the AJCC Cancer Staging Manual, current edition for site-specific categories for the TNM elements and stage groups. See the STORE manual for specifications for codes and data entry rules.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#970
All data
st_css() #IMPORTANT!
tnmclinstagegroup <- as.factor(trimws(d[,"tnmclinstagegroup"]))
levels(tnmclinstagegroup)[levels(tnmclinstagegroup)=="88"] <- "Not_applicable.88"
levels(tnmclinstagegroup)[levels(tnmclinstagegroup)=="99"] <- "Unknown.99"
new.d <- data.frame(new.d, tnmclinstagegroup)
new.d <- apply_labels(new.d, tnmclinstagegroup = "tnm_clin_stage_group")
temp.d <- data.frame (new.d.1, tnmclinstagegroup)
summarytools::view(dfSummary(new.d$tnmclinstagegroup, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnmclinstagegroup
[labelled, factor] |
tnm_clin_stage_group |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 352 | ( | 36.9% | ) | | 22 | ( | 2.3% | ) | | 179 | ( | 18.7% | ) | | 126 | ( | 13.2% | ) | | 45 | ( | 4.7% | ) | | 78 | ( | 8.2% | ) | | 153 | ( | 16.0% | ) |
|
 |
1212
(55.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 31 | ( | 54.4% | ) | | 3 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 5.3% | ) | | 5 | ( | 8.8% | ) | | 15 | ( | 26.3% | ) |
|
 |
132
(69.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 37 | ( | 67.3% | ) | | 6 | ( | 10.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.6% | ) | | 8 | ( | 14.5% | ) | | 2 | ( | 3.6% | ) |
|
 |
119
(68.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 32 | ( | 49.2% | ) | | 4 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 6.2% | ) | | 14 | ( | 21.5% | ) | | 11 | ( | 16.9% | ) |
|
 |
172
(72.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 17 | ( | 12.8% | ) | | 0 | ( | 0.0% | ) | | 62 | ( | 46.6% | ) | | 36 | ( | 27.1% | ) | | 2 | ( | 1.5% | ) | | 7 | ( | 5.3% | ) | | 9 | ( | 6.8% | ) |
|
 |
41
(23.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 46 | ( | 16.1% | ) | | 3 | ( | 1.1% | ) | | 111 | ( | 38.9% | ) | | 90 | ( | 31.6% | ) | | 11 | ( | 3.9% | ) | | 7 | ( | 2.5% | ) | | 17 | ( | 6.0% | ) |
|
 |
5
(1.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 186 | ( | 53.3% | ) | | 6 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 6.6% | ) | | 37 | ( | 10.6% | ) | | 97 | ( | 27.8% | ) |
|
 |
738
(67.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tnm_clin_stage_group
[factor] |
1. 1
2. 2
3. 2A
4. 2B
5. 3
6. 4
7. Unknown.99 |
| 3 | ( | 27.3% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 54.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 18.2% | ) |
|
 |
5
(31.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AJCC TNM CLIN T
- Description: Detailed site-specific codes for the clinical tumor (T) as defined by the current AJCC edition.
- Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
- Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- Blank Information not available to code this item.
- Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1001
All data
st_css() #IMPORTANT!
ajcctnmclint <- as.factor(trimws(d[,"ajcctnmclint"]))
levels(ajcctnmclint)[levels(ajcctnmclint)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, ajcctnmclint)
new.d <- apply_labels(new.d, ajcctnmclint = "ajcc_tnm_clin_t")
temp.d <- data.frame (new.d.1, ajcctnmclint)
summarytools::view(dfSummary(new.d$ajcctnmclint, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcctnmclint
[labelled, factor] |
ajcc_tnm_clin_t |
1. cT1a
2. cT1c
3. cT2
4. cT2a
5. cT3a
6. cTX |
| 1 | ( | 5.0% | ) | | 11 | ( | 55.0% | ) | | 2 | ( | 10.0% | ) | | 4 | ( | 20.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) |
|
 |
2147
(99.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_t
[factor] |
1. cT1a
2. cT1c
3. cT2
4. cT2a
5. cT3a
6. cTX |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_t
[factor] |
1. cT1a
2. cT1c
3. cT2
4. cT2a
5. cT3a
6. cTX |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_t
[factor] |
1. cT1a
2. cT1c
3. cT2
4. cT2a
5. cT3a
6. cTX |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_t
[factor] |
1. cT1a
2. cT1c
3. cT2
4. cT2a
5. cT3a
6. cTX |
| 1 | ( | 5.3% | ) | | 10 | ( | 52.6% | ) | | 2 | ( | 10.5% | ) | | 4 | ( | 21.1% | ) | | 1 | ( | 5.3% | ) | | 1 | ( | 5.3% | ) |
|
 |
155
(89.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_t
[factor] |
1. cT1a
2. cT1c
3. cT2
4. cT2a
5. cT3a
6. cTX |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_t
[factor] |
1. cT1a
2. cT1c
3. cT2
4. cT2a
5. cT3a
6. cTX |
| 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_t
[factor] |
1. cT1a
2. cT1c
3. cT2
4. cT2a
5. cT3a
6. cTX |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AJCC TNM CLIN N
- Description: Detailed site-specific codes for the clinical nodes (N) as defined by the current AJCC edition.
- Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
- Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- Blank Information not available to code this item.
- Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1002
All data
st_css() #IMPORTANT!
ajcctnmclinn <- as.factor(trimws(d[,"ajcctnmclinn"]))
levels(ajcctnmclinn)[levels(ajcctnmclinn)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, ajcctnmclinn)
new.d <- apply_labels(new.d, ajcctnmclinn = "ajcc_tnm_clin_n")
temp.d <- data.frame (new.d.1, ajcctnmclinn)
summarytools::view(dfSummary(new.d$ajcctnmclinn, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcctnmclinn
[labelled, factor] |
ajcc_tnm_clin_n |
1. cN0
2. cN1
3. cNX |
|
 |
2146
(99.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_n
[factor] |
1. cN0
2. cN1
3. cNX |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_n
[factor] |
1. cN0
2. cN1
3. cNX |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_n
[factor] |
1. cN0
2. cN1
3. cNX |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_n
[factor] |
1. cN0
2. cN1
3. cNX |
|
 |
154
(88.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_n
[factor] |
1. cN0
2. cN1
3. cNX |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_n
[factor] |
1. cN0
2. cN1
3. cNX |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_n
[factor] |
1. cN0
2. cN1
3. cNX |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AJCC TNM CLIN M
- Description: Detailed site-specific codes for the clinical metastases (M) as defined by the current AJCC edition.
- Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
- Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- Blank Information not available to code this item.
- Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1003
All data
st_css() #IMPORTANT!
ajcctnmclinm <- as.factor(trimws(d[,"ajcctnmclinm"]))
levels(ajcctnmclinm)[levels(ajcctnmclinm)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, ajcctnmclinm)
new.d <- apply_labels(new.d, ajcctnmclinm = "ajcc_tnm_clin_m")
temp.d <- data.frame (new.d.1, ajcctnmclinm)
summarytools::view(dfSummary(new.d$ajcctnmclinm, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcctnmclinm
[labelled, factor] |
ajcc_tnm_clin_m |
1. cM0
2. cM1b
3. cM1c |
|
 |
2146
(99.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_m
[factor] |
1. cM0
2. cM1b
3. cM1c |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_m
[factor] |
1. cM0
2. cM1b
3. cM1c |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_m
[factor] |
1. cM0
2. cM1b
3. cM1c |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_m
[factor] |
1. cM0
2. cM1b
3. cM1c |
|
 |
154
(88.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_m
[factor] |
1. cM0
2. cM1b
3. cM1c |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_m
[factor] |
1. cM0
2. cM1b
3. cM1c |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_m
[factor] |
1. cM0
2. cM1b
3. cM1c |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AJCC TNM CLIN STAGE GROUP
- Description: Detailed site-specific codes for the clinical stage group as defined by the current AJCC edition.
- Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
- Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- 99 Unknown, not staged
- Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1004
All data
st_css() #IMPORTANT!
ajcctnmclinstagegroup <- as.factor(trimws(d[,"ajcctnmclinstagegroup"]))
levels(ajcctnmclinstagegroup)[levels(ajcctnmclinstagegroup)=="88"] <- "Not_applicable.88"
levels(ajcctnmclinstagegroup)[levels(ajcctnmclinstagegroup)=="99"] <- "Unknown.99"
new.d <- data.frame(new.d, ajcctnmclinstagegroup)
new.d <- apply_labels(new.d, ajcctnmclinstagegroup = "ajcc_tnm_clin_stage_group")
temp.d <- data.frame (new.d.1, ajcctnmclinstagegroup)
summarytools::view(dfSummary(new.d$ajcctnmclinstagegroup, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcctnmclinstagegroup
[labelled, factor] |
ajcc_tnm_clin_stage_group |
1. 1
2. 2A
3. 2B
4. 2C
5. 3A
6. 3B
7. 3C
8. 4B
9. Unknown.99 |
| 4 | ( | 14.3% | ) | | 2 | ( | 7.1% | ) | | 3 | ( | 10.7% | ) | | 5 | ( | 17.9% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 2 | ( | 7.1% | ) | | 9 | ( | 32.1% | ) |
|
 |
2139
(98.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_stage_group
[factor] |
1. 1
2. 2A
3. 2B
4. 2C
5. 3A
6. 3B
7. 3C
8. 4B
9. Unknown.99 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_stage_group
[factor] |
1. 1
2. 2A
3. 2B
4. 2C
5. 3A
6. 3B
7. 3C
8. 4B
9. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_stage_group
[factor] |
1. 1
2. 2A
3. 2B
4. 2C
5. 3A
6. 3B
7. 3C
8. 4B
9. Unknown.99 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_stage_group
[factor] |
1. 1
2. 2A
3. 2B
4. 2C
5. 3A
6. 3B
7. 3C
8. 4B
9. Unknown.99 |
| 3 | ( | 11.5% | ) | | 2 | ( | 7.7% | ) | | 3 | ( | 11.5% | ) | | 5 | ( | 19.2% | ) | | 1 | ( | 3.8% | ) | | 1 | ( | 3.8% | ) | | 1 | ( | 3.8% | ) | | 2 | ( | 7.7% | ) | | 8 | ( | 30.8% | ) |
|
 |
148
(85.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_stage_group
[factor] |
1. 1
2. 2A
3. 2B
4. 2C
5. 3A
6. 3B
7. 3C
8. 4B
9. Unknown.99 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_stage_group
[factor] |
1. 1
2. 2A
3. 2B
4. 2C
5. 3A
6. 3B
7. 3C
8. 4B
9. Unknown.99 |
| 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_clin_stage_group
[factor] |
1. 1
2. 2A
3. 2B
4. 2C
5. 3A
6. 3B
7. 3C
8. 4B
9. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AJCC TNM PATH T
- Description: Detailed site-specific codes for the pathologic tumor (T) as defined by the current AJCC edition.
- Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
- Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- Blank Information not available to code this item.
- Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1011
All data
st_css() #IMPORTANT!
ajcctnmpatht <- as.factor(trimws(d[,"ajcctnmpatht"]))
levels(ajcctnmpatht)[levels(ajcctnmpatht)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, ajcctnmpatht)
new.d <- apply_labels(new.d, ajcctnmpatht = "ajcc_tnm_path_t")
temp.d <- data.frame (new.d.1, ajcctnmpatht)
summarytools::view(dfSummary(new.d$ajcctnmpatht, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcctnmpatht
[labelled, factor] |
ajcc_tnm_path_t |
1. pT1b
2. pT2
3. pT3a |
|
 |
2162
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_t
[factor] |
1. pT1b
2. pT2
3. pT3a |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_t
[factor] |
1. pT1b
2. pT2
3. pT3a |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_t
[factor] |
1. pT1b
2. pT2
3. pT3a |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_t
[factor] |
1. pT1b
2. pT2
3. pT3a |
|
 |
170
(97.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_t
[factor] |
1. pT1b
2. pT2
3. pT3a |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_t
[factor] |
1. pT1b
2. pT2
3. pT3a |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_t
[factor] |
1. pT1b
2. pT2
3. pT3a |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AJCC TNM PATH N
- Description: Detailed site-specific codes for the pathologic nodes (N) as defined by the current AJCC edition.
- Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
- Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- Blank Information not available to code this item.
- Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1012
All data
st_css() #IMPORTANT!
ajcctnmpathn <- as.factor(trimws(d[,"ajcctnmpathn"]))
levels(ajcctnmpathn)[levels(ajcctnmpathn)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, ajcctnmpathn)
new.d <- apply_labels(new.d, ajcctnmpathn = "ajcc_tnm_path_n")
temp.d <- data.frame (new.d.1, ajcctnmpathn)
summarytools::view(dfSummary(new.d$ajcctnmpathn, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcctnmpathn
[labelled, factor] |
ajcc_tnm_path_n |
1. pN0
2. pNX |
|
 |
2162
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_n
[factor] |
1. pN0
2. pNX |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_n
[factor] |
1. pN0
2. pNX |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_n
[factor] |
1. pN0
2. pNX |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_n
[factor] |
1. pN0
2. pNX |
|
 |
170
(97.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_n
[factor] |
1. pN0
2. pNX |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_n
[factor] |
1. pN0
2. pNX |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_n
[factor] |
1. pN0
2. pNX |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AJCC TNM PATH M
- Description: Detailed site-specific codes for the clinical path (M) as defined by the current AJCC edition.
- Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan trajcctnmpathneatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
- Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- Blank Information not available to code this item.
- Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1013
All data
st_css() #IMPORTANT!
ajcctnmpathm <- as.factor(trimws(d[,"ajcctnmpathm"]))
levels(ajcctnmpathm)[levels(ajcctnmpathm)=="88"] <- "Not_applicable.88"
new.d <- data.frame(new.d, ajcctnmpathm)
new.d <- apply_labels(new.d, ajcctnmpathm = "ajcc_tnm_path_m")
temp.d <- data.frame (new.d.1, ajcctnmpathm)
summarytools::view(dfSummary(new.d$ajcctnmpathm, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcctnmpathm
[labelled, factor] |
ajcc_tnm_path_m |
1. cM0 |
|
 |
2162
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_m
[factor] |
1. cM0 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_m
[factor] |
1. cM0 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_m
[factor] |
1. cM0 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_m
[factor] |
1. cM0 |
|
 |
170
(97.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_m
[factor] |
1. cM0 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_m
[factor] |
1. cM0 |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_m
[factor] |
1. cM0 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
AJCC TNM PATH STAGE GROUP
- Description: Detailed site-specific codes for the pathologic stage group as defined by the current AJCC edition.
- Rationale: CoC requires that AJCC TNM staging be used in its approved cancer programs. AJCC developed its staging system for evaluating trends in the treatment and control of cancer. This staging is used by physicians to estimate prognosis, to plan treatment, to evaluate new types of therapy, to analyze outcome, to design follow-up strategies, and to assess early detection results.
- Codes (in addition to those published in the AJCC Cancer Staging Manual)
- 88 Not applicable, no code assigned for this case in the current AJCC Staging Manual.
- 99 Unknown, not staged
- Note: See the AJCC Cancer Staging Manual, 8th edition for site-specific categories for the TNM elements and stage groups. See the CURRENT STORE manual for specifications for codes and data entry rules.
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1014
All data
st_css() #IMPORTANT!
ajcctnmpathstagegroup <- as.factor(trimws(d[,"ajcctnmpathstagegroup"]))
levels(ajcctnmpathstagegroup)[levels(ajcctnmpathstagegroup)=="88"] <- "Not_applicable.88"
levels(ajcctnmpathstagegroup)[levels(ajcctnmpathstagegroup)=="99"] <- "Unknown.99"
new.d <- data.frame(new.d, ajcctnmpathstagegroup)
new.d <- apply_labels(new.d, ajcctnmpathstagegroup = "ajcc_tnm_path_stage_group")
temp.d <- data.frame (new.d.1, ajcctnmpathstagegroup)
summarytools::view(dfSummary(new.d$ajcctnmpathstagegroup, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcctnmpathstagegroup
[labelled, factor] |
ajcc_tnm_path_stage_group |
1. 1B
2. 3B
3. 3C
4. Unknown.99 |
| 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 25 | ( | 89.3% | ) |
|
 |
2139
(98.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_stage_group
[factor] |
1. 1B
2. 3B
3. 3C
4. Unknown.99 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_stage_group
[factor] |
1. 1B
2. 3B
3. 3C
4. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_stage_group
[factor] |
1. 1B
2. 3B
3. 3C
4. Unknown.99 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_stage_group
[factor] |
1. 1B
2. 3B
3. 3C
4. Unknown.99 |
| 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 1 | ( | 3.8% | ) | | 24 | ( | 92.3% | ) |
|
 |
148
(85.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_stage_group
[factor] |
1. 1B
2. 3B
3. 3C
4. Unknown.99 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_stage_group
[factor] |
1. 1B
2. 3B
3. 3C
4. Unknown.99 |
| 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
ajcc_tnm_path_stage_group
[factor] |
1. 1B
2. 3B
3. 3C
4. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
TUMOR MARKER 2
Description: Records prognostic indicators for specific sites or histologies. CoC offered these items for optional use for cases diagnosed 1996 and forward. See the CoC ROADS Manual, 1998 Supplement, for a list of specific sites and histologies.
For SEER requirements for the specific sites, histologies, and diagnosis years for which this item is coded, see the 1998 SEER Program Code Manual.
Codes
- 0 None done (SX)
- 1 Positive/elevated
- 2 Negative/normal; within normal limits (S0)
- 3 Borderline; undetermined whether positive/elevated or negative/normal
Three-tiered system:
- 4 Range 1 (S1)
- 5 Range 2 (S2)
- 6 Range 3 (S3)
- 8 Ordered, but results not in chart
- 9 Not applicable
For sites for which Tumor Marker 2 is not collected:
Note: As of January 1, 2003, this data item is no longer required or recommended by CoC. However, the item was collected in the past and it is recommended that historic data be retained.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1160
All data
st_css() #IMPORTANT!
tumormarker2 <- as.factor(trimws(d[,"tumormarker2"]))
new.d <- data.frame(new.d, tumormarker2)
new.d <- apply_labels(new.d, tumormarker2 = "tumor_marker2")
temp.d <- data.frame (new.d.1, tumormarker2)
summarytools::view(dfSummary(new.d$tumormarker2, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
tumormarker2
[labelled, factor] |
tumor_marker2 |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE SURGERY
Description: Date the first surgery of the type described under Surgery of Primary Site, Scope of Regional Lymph Node Surgery, or Surgery of Other Regional Site(s), Distant Site(s) or Distant Lymph Nodes was performed. See also RX Summ–Surg Prim Site [1290], RX Summ–Scope Reg LN Sur [1292], and RX Summ–Surg Oth Reg/Dis [1294]. See Chapter X for date format. Formerly RX Date–Surgery.
Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1200
All data
st_css() #IMPORTANT!
rxdatesurgery <- as.factor(d[,"rxdatesurgery"])
new.d <- data.frame(new.d, rxdatesurgery)
new.d <- apply_labels(new.d, rxdatesurgery = "rx_date_surgery")
#summary(new.d$rxdatesurgery)
temp.d <- data.frame (new.d.1, rxdatesurgery)
summarytools::view(dfSummary(new.d$rxdatesurgery, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdatesurgery
[labelled, factor] |
rx_date_surgery |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150522
43. 20150527
44. 20150528
45. 20150599
46. 201506 ·
47. 20150601
48. 20150603
49. 20150605
50. 20150610
[ 417 others ] |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 7 | ( | 0.7% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 11 | ( | 1.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 923 | ( | 90.0% | ) |
|
 |
1142
(52.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_surgery
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150522
43. 20150527
44. 20150528
45. 20150599
46. 201506 ·
47. 20150601
48. 20150603
49. 20150605
50. 20150610
[ 417 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 4.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 91 | ( | 86.7% | ) |
|
 |
84
(44.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_surgery
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150522
43. 20150527
44. 20150528
45. 20150599
46. 201506 ·
47. 20150601
48. 20150603
49. 20150605
50. 20150610
[ 417 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 45 | ( | 93.8% | ) |
|
 |
126
(72.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_surgery
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150522
43. 20150527
44. 20150528
45. 20150599
46. 201506 ·
47. 20150601
48. 20150603
49. 20150605
50. 20150610
[ 417 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 128 | ( | 88.9% | ) |
|
 |
93
(39.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_surgery
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150522
43. 20150527
44. 20150528
45. 20150599
46. 201506 ·
47. 20150601
48. 20150603
49. 20150605
50. 20150610
[ 417 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 78 | ( | 91.8% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_surgery
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150522
43. 20150527
44. 20150528
45. 20150599
46. 201506 ·
47. 20150601
48. 20150603
49. 20150605
50. 20150610
[ 417 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 160 | ( | 87.9% | ) |
|
 |
108
(37.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_surgery
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150522
43. 20150527
44. 20150528
45. 20150599
46. 201506 ·
47. 20150601
48. 20150603
49. 20150605
50. 20150610
[ 417 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 3 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.7% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 421 | ( | 92.7% | ) |
|
 |
633
(58.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_surgery
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150522
43. 20150527
44. 20150528
45. 20150599
46. 201506 ·
47. 20150601
48. 20150603
49. 20150605
50. 20150610
[ 417 others ] |
| 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
9
(56.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE RADIATION
Description: Records the date on which radiation therapy began at any facility that is part of the first course of treatment. See Chapter X for date format. Use RX DATE RADIATION FLAG [1211] if there is no appropriate or known date for this item. Formerly RX Date–Radiation
Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1210
All data
st_css() #IMPORTANT!
rxdateradiation <- as.factor(d[,"rxdateradiation"])
new.d <- data.frame(new.d, rxdateradiation)
new.d <- apply_labels(new.d, rxdateradiation = "rx_date_radiation")
#summary(new.d$rxdateradiation)
temp.d <- data.frame (new.d.1, rxdateradiation)
summarytools::view(dfSummary(new.d$rxdateradiation, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdateradiation
[labelled, factor] |
rx_date_radiation |
1. 20130599
2. 20130699
3. 20140799
4. 20150199
5. 20150205
6. 20150224
7. 201503 ·
8. 20150317
9. 20150323
10. 20150326
11. 201504 ·
12. 20150401
13. 20150407
14. 20150409
15. 20150420
16. 20150423
17. 20150428
18. 20150429
19. 20150499
20. 201505 ·
21. 20150504
22. 20150506
23. 20150509
24. 20150511
25. 20150514
26. 20150520
27. 20150521
28. 20150526
29. 20150527
30. 201506 ·
31. 20150601
32. 20150602
33. 20150606
34. 20150608
35. 20150609
36. 20150615
37. 20150617
38. 20150618
39. 20150622
40. 20150625
41. 20150699
42. 201507 ·
43. 20150701
44. 20150706
45. 20150708
46. 20150713
47. 20150714
48. 20150715
49. 20150716
50. 20150720
[ 381 others ] |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.7% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.7% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.6% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 747 | ( | 89.4% | ) |
|
 |
1331
(61.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_radiation
[factor] |
1. 20130599
2. 20130699
3. 20140799
4. 20150199
5. 20150205
6. 20150224
7. 201503 ·
8. 20150317
9. 20150323
10. 20150326
11. 201504 ·
12. 20150401
13. 20150407
14. 20150409
15. 20150420
16. 20150423
17. 20150428
18. 20150429
19. 20150499
20. 201505 ·
21. 20150504
22. 20150506
23. 20150509
24. 20150511
25. 20150514
26. 20150520
27. 20150521
28. 20150526
29. 20150527
30. 201506 ·
31. 20150601
32. 20150602
33. 20150606
34. 20150608
35. 20150609
36. 20150615
37. 20150617
38. 20150618
39. 20150622
40. 20150625
41. 20150699
42. 201507 ·
43. 20150701
44. 20150706
45. 20150708
46. 20150713
47. 20150714
48. 20150715
49. 20150716
50. 20150720
[ 381 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 6.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 4.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 40 | ( | 85.1% | ) |
|
 |
142
(75.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_radiation
[factor] |
1. 20130599
2. 20130699
3. 20140799
4. 20150199
5. 20150205
6. 20150224
7. 201503 ·
8. 20150317
9. 20150323
10. 20150326
11. 201504 ·
12. 20150401
13. 20150407
14. 20150409
15. 20150420
16. 20150423
17. 20150428
18. 20150429
19. 20150499
20. 201505 ·
21. 20150504
22. 20150506
23. 20150509
24. 20150511
25. 20150514
26. 20150520
27. 20150521
28. 20150526
29. 20150527
30. 201506 ·
31. 20150601
32. 20150602
33. 20150606
34. 20150608
35. 20150609
36. 20150615
37. 20150617
38. 20150618
39. 20150622
40. 20150625
41. 20150699
42. 201507 ·
43. 20150701
44. 20150706
45. 20150708
46. 20150713
47. 20150714
48. 20150715
49. 20150716
50. 20150720
[ 381 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 72 | ( | 92.3% | ) |
|
 |
96
(55.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_radiation
[factor] |
1. 20130599
2. 20130699
3. 20140799
4. 20150199
5. 20150205
6. 20150224
7. 201503 ·
8. 20150317
9. 20150323
10. 20150326
11. 201504 ·
12. 20150401
13. 20150407
14. 20150409
15. 20150420
16. 20150423
17. 20150428
18. 20150429
19. 20150499
20. 201505 ·
21. 20150504
22. 20150506
23. 20150509
24. 20150511
25. 20150514
26. 20150520
27. 20150521
28. 20150526
29. 20150527
30. 201506 ·
31. 20150601
32. 20150602
33. 20150606
34. 20150608
35. 20150609
36. 20150615
37. 20150617
38. 20150618
39. 20150622
40. 20150625
41. 20150699
42. 201507 ·
43. 20150701
44. 20150706
45. 20150708
46. 20150713
47. 20150714
48. 20150715
49. 20150716
50. 20150720
[ 381 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 53 | ( | 89.8% | ) |
|
 |
178
(75.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_radiation
[factor] |
1. 20130599
2. 20130699
3. 20140799
4. 20150199
5. 20150205
6. 20150224
7. 201503 ·
8. 20150317
9. 20150323
10. 20150326
11. 201504 ·
12. 20150401
13. 20150407
14. 20150409
15. 20150420
16. 20150423
17. 20150428
18. 20150429
19. 20150499
20. 201505 ·
21. 20150504
22. 20150506
23. 20150509
24. 20150511
25. 20150514
26. 20150520
27. 20150521
28. 20150526
29. 20150527
30. 201506 ·
31. 20150601
32. 20150602
33. 20150606
34. 20150608
35. 20150609
36. 20150615
37. 20150617
38. 20150618
39. 20150622
40. 20150625
41. 20150699
42. 201507 ·
43. 20150701
44. 20150706
45. 20150708
46. 20150713
47. 20150714
48. 20150715
49. 20150716
50. 20150720
[ 381 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 57 | ( | 95.0% | ) |
|
 |
114
(65.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_radiation
[factor] |
1. 20130599
2. 20130699
3. 20140799
4. 20150199
5. 20150205
6. 20150224
7. 201503 ·
8. 20150317
9. 20150323
10. 20150326
11. 201504 ·
12. 20150401
13. 20150407
14. 20150409
15. 20150420
16. 20150423
17. 20150428
18. 20150429
19. 20150499
20. 201505 ·
21. 20150504
22. 20150506
23. 20150509
24. 20150511
25. 20150514
26. 20150520
27. 20150521
28. 20150526
29. 20150527
30. 201506 ·
31. 20150601
32. 20150602
33. 20150606
34. 20150608
35. 20150609
36. 20150615
37. 20150617
38. 20150618
39. 20150622
40. 20150625
41. 20150699
42. 201507 ·
43. 20150701
44. 20150706
45. 20150708
46. 20150713
47. 20150714
48. 20150715
49. 20150716
50. 20150720
[ 381 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 89 | ( | 91.8% | ) |
|
 |
193
(66.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_radiation
[factor] |
1. 20130599
2. 20130699
3. 20140799
4. 20150199
5. 20150205
6. 20150224
7. 201503 ·
8. 20150317
9. 20150323
10. 20150326
11. 201504 ·
12. 20150401
13. 20150407
14. 20150409
15. 20150420
16. 20150423
17. 20150428
18. 20150429
19. 20150499
20. 201505 ·
21. 20150504
22. 20150506
23. 20150509
24. 20150511
25. 20150514
26. 20150520
27. 20150521
28. 20150526
29. 20150527
30. 201506 ·
31. 20150601
32. 20150602
33. 20150606
34. 20150608
35. 20150609
36. 20150615
37. 20150617
38. 20150618
39. 20150622
40. 20150625
41. 20150699
42. 201507 ·
43. 20150701
44. 20150706
45. 20150708
46. 20150713
47. 20150714
48. 20150715
49. 20150716
50. 20150720
[ 381 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 3 | ( | 0.6% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 5 | ( | 1.0% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 3 | ( | 0.6% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 435 | ( | 88.8% | ) |
|
 |
597
(54.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_radiation
[factor] |
1. 20130599
2. 20130699
3. 20140799
4. 20150199
5. 20150205
6. 20150224
7. 201503 ·
8. 20150317
9. 20150323
10. 20150326
11. 201504 ·
12. 20150401
13. 20150407
14. 20150409
15. 20150420
16. 20150423
17. 20150428
18. 20150429
19. 20150499
20. 201505 ·
21. 20150504
22. 20150506
23. 20150509
24. 20150511
25. 20150514
26. 20150520
27. 20150521
28. 20150526
29. 20150527
30. 201506 ·
31. 20150601
32. 20150602
33. 20150606
34. 20150608
35. 20150609
36. 20150615
37. 20150617
38. 20150618
39. 20150622
40. 20150625
41. 20150699
42. 201507 ·
43. 20150701
44. 20150706
45. 20150708
46. 20150713
47. 20150714
48. 20150715
49. 20150716
50. 20150720
[ 381 others ] |
| 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) |
|
 |
11
(68.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE CHEMO
Description: Date of initiation of chemotherapy that is part of the first course of treatment. See also RX Summ–Chemo [1390]. See Chapter X for date format. Formerly RX Date–Chemo.
Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes. Note: CoC discontinued support of this item from 2003 through 2009.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1220
All data
st_css() #IMPORTANT!
rxdatechemo <- as.factor(d[,"rxdatechemo"])
new.d <- data.frame(new.d, rxdatechemo)
new.d <- apply_labels(new.d, rxdatechemo = "rx_date_chemo")
#summary(new.d$rxdatechemo)
temp.d <- data.frame (new.d.1, rxdatechemo)
summarytools::view(dfSummary(new.d$rxdatechemo, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdatechemo
[labelled, factor] |
rx_date_chemo |
1. 201503 ·
2. 20150423
3. 20150515
4. 201509 ·
5. 20151022
6. 20151202
7. 20160120
8. 20160223
9. 201604 ·
10. 201606 ·
11. 20160608
12. 20160720
13. 201608 ·
14. 201610 ·
15. 20161021
16. 20161111
17. 20161122
18. 20161130
19. 201612 ·
20. 201701 ·
21. 20170119
22. 201702 ·
23. 20170202
24. 20170516
25. 20170601
26. 20180403 |
| 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 4 | ( | 13.8% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) | | 1 | ( | 3.4% | ) |
|
 |
2138
(98.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_chemo
[factor] |
1. 201503 ·
2. 20150423
3. 20150515
4. 201509 ·
5. 20151022
6. 20151202
7. 20160120
8. 20160223
9. 201604 ·
10. 201606 ·
11. 20160608
12. 20160720
13. 201608 ·
14. 201610 ·
15. 20161021
16. 20161111
17. 20161122
18. 20161130
19. 201612 ·
20. 201701 ·
21. 20170119
22. 201702 ·
23. 20170202
24. 20170516
25. 20170601
26. 20180403 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
188
(99.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_chemo
[factor] |
1. 201503 ·
2. 20150423
3. 20150515
4. 201509 ·
5. 20151022
6. 20151202
7. 20160120
8. 20160223
9. 201604 ·
10. 201606 ·
11. 20160608
12. 20160720
13. 201608 ·
14. 201610 ·
15. 20161021
16. 20161111
17. 20161122
18. 20161130
19. 201612 ·
20. 201701 ·
21. 20170119
22. 201702 ·
23. 20170202
24. 20170516
25. 20170601
26. 20180403 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 75.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
170
(97.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_chemo
[factor] |
1. 201503 ·
2. 20150423
3. 20150515
4. 201509 ·
5. 20151022
6. 20151202
7. 20160120
8. 20160223
9. 201604 ·
10. 201606 ·
11. 20160608
12. 20160720
13. 201608 ·
14. 201610 ·
15. 20161021
16. 20161111
17. 20161122
18. 20161130
19. 201612 ·
20. 201701 ·
21. 20170119
22. 201702 ·
23. 20170202
24. 20170516
25. 20170601
26. 20180403 |
| 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
230
(97.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_chemo
[factor] |
1. 201503 ·
2. 20150423
3. 20150515
4. 201509 ·
5. 20151022
6. 20151202
7. 20160120
8. 20160223
9. 201604 ·
10. 201606 ·
11. 20160608
12. 20160720
13. 201608 ·
14. 201610 ·
15. 20161021
16. 20161111
17. 20161122
18. 20161130
19. 201612 ·
20. 201701 ·
21. 20170119
22. 201702 ·
23. 20170202
24. 20170516
25. 20170601
26. 20180403 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_chemo
[factor] |
1. 201503 ·
2. 20150423
3. 20150515
4. 201509 ·
5. 20151022
6. 20151202
7. 20160120
8. 20160223
9. 201604 ·
10. 201606 ·
11. 20160608
12. 20160720
13. 201608 ·
14. 201610 ·
15. 20161021
16. 20161111
17. 20161122
18. 20161130
19. 201612 ·
20. 201701 ·
21. 20170119
22. 201702 ·
23. 20170202
24. 20170516
25. 20170601
26. 20180403 |
| 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
289
(99.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_chemo
[factor] |
1. 201503 ·
2. 20150423
3. 20150515
4. 201509 ·
5. 20151022
6. 20151202
7. 20160120
8. 20160223
9. 201604 ·
10. 201606 ·
11. 20160608
12. 20160720
13. 201608 ·
14. 201610 ·
15. 20161021
16. 20161111
17. 20161122
18. 20161130
19. 201612 ·
20. 201701 ·
21. 20170119
22. 201702 ·
23. 20170202
24. 20170516
25. 20170601
26. 20180403 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) |
|
 |
1071
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_chemo
[factor] |
1. 201503 ·
2. 20150423
3. 20150515
4. 201509 ·
5. 20151022
6. 20151202
7. 20160120
8. 20160223
9. 201604 ·
10. 201606 ·
11. 20160608
12. 20160720
13. 201608 ·
14. 201610 ·
15. 20161021
16. 20161111
17. 20161122
18. 20161130
19. 201612 ·
20. 201701 ·
21. 20170119
22. 201702 ·
23. 20170202
24. 20170516
25. 20170601
26. 20180403 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE HORMONE
Description: Date of initiation for hormone therapy that is part of the first course of treatment. See also RX Summ–Hormone [1400]. See Chapter X for date format. Formerly RX Date–Hormone.
Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes. Note: CoC discontinued support of this item from 2003 through 2009.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1230
All data
st_css() #IMPORTANT!
rxdatehormone <- as.factor(d[,"rxdatehormone"])
new.d <- data.frame(new.d, rxdatehormone)
new.d <- apply_labels(new.d, rxdatehormone = "rx_date_hormone")
#summary(new.d$rxdatehormone)
temp.d <- data.frame (new.d.1, rxdatehormone)
summarytools::view(dfSummary(new.d$rxdatehormone, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdatehormone
[labelled, factor] |
rx_date_hormone |
1. 20130499
2. 201501 ·
3. 20150116
4. 20150126
5. 201502 ·
6. 20150210
7. 20150211
8. 20150217
9. 201503 ·
10. 20150306
11. 20150326
12. 20150399
13. 201504 ·
14. 20150401
15. 20150410
16. 20150415
17. 20150428
18. 20150499
19. 201505 ·
20. 20150504
21. 20150505
22. 20150507
23. 20150509
24. 20150514
25. 20150519
26. 20150521
27. 20150526
28. 20150528
29. 201506 ·
30. 20150601
31. 20150602
32. 20150604
33. 20150608
34. 20150611
35. 20150612
36. 20150615
37. 20150616
38. 20150618
39. 20150619
40. 20150623
41. 20150625
42. 20150626
43. 20150627
44. 20150699
45. 20150702
46. 20150709
47. 20150727
48. 20150799
49. 201508 ·
50. 20150801
[ 235 others ] |
| 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 5 | ( | 1.1% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 6 | ( | 1.3% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 4 | ( | 0.9% | ) | | 1 | ( | 0.2% | ) | | 399 | ( | 85.1% | ) |
|
 |
1698
(78.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_hormone
[factor] |
1. 20130499
2. 201501 ·
3. 20150116
4. 20150126
5. 201502 ·
6. 20150210
7. 20150211
8. 20150217
9. 201503 ·
10. 20150306
11. 20150326
12. 20150399
13. 201504 ·
14. 20150401
15. 20150410
16. 20150415
17. 20150428
18. 20150499
19. 201505 ·
20. 20150504
21. 20150505
22. 20150507
23. 20150509
24. 20150514
25. 20150519
26. 20150521
27. 20150526
28. 20150528
29. 201506 ·
30. 20150601
31. 20150602
32. 20150604
33. 20150608
34. 20150611
35. 20150612
36. 20150615
37. 20150616
38. 20150618
39. 20150619
40. 20150623
41. 20150625
42. 20150626
43. 20150627
44. 20150699
45. 20150702
46. 20150709
47. 20150727
48. 20150799
49. 201508 ·
50. 20150801
[ 235 others ] |
| 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 32 | ( | 86.5% | ) |
|
 |
152
(80.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_hormone
[factor] |
1. 20130499
2. 201501 ·
3. 20150116
4. 20150126
5. 201502 ·
6. 20150210
7. 20150211
8. 20150217
9. 201503 ·
10. 20150306
11. 20150326
12. 20150399
13. 201504 ·
14. 20150401
15. 20150410
16. 20150415
17. 20150428
18. 20150499
19. 201505 ·
20. 20150504
21. 20150505
22. 20150507
23. 20150509
24. 20150514
25. 20150519
26. 20150521
27. 20150526
28. 20150528
29. 201506 ·
30. 20150601
31. 20150602
32. 20150604
33. 20150608
34. 20150611
35. 20150612
36. 20150615
37. 20150616
38. 20150618
39. 20150619
40. 20150623
41. 20150625
42. 20150626
43. 20150627
44. 20150699
45. 20150702
46. 20150709
47. 20150727
48. 20150799
49. 201508 ·
50. 20150801
[ 235 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 41 | ( | 89.1% | ) |
|
 |
128
(73.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_hormone
[factor] |
1. 20130499
2. 201501 ·
3. 20150116
4. 20150126
5. 201502 ·
6. 20150210
7. 20150211
8. 20150217
9. 201503 ·
10. 20150306
11. 20150326
12. 20150399
13. 201504 ·
14. 20150401
15. 20150410
16. 20150415
17. 20150428
18. 20150499
19. 201505 ·
20. 20150504
21. 20150505
22. 20150507
23. 20150509
24. 20150514
25. 20150519
26. 20150521
27. 20150526
28. 20150528
29. 201506 ·
30. 20150601
31. 20150602
32. 20150604
33. 20150608
34. 20150611
35. 20150612
36. 20150615
37. 20150616
38. 20150618
39. 20150619
40. 20150623
41. 20150625
42. 20150626
43. 20150627
44. 20150699
45. 20150702
46. 20150709
47. 20150727
48. 20150799
49. 201508 ·
50. 20150801
[ 235 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 7.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 7.0% | ) | | 0 | ( | 0.0% | ) | | 34 | ( | 79.1% | ) |
|
 |
194
(81.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_hormone
[factor] |
1. 20130499
2. 201501 ·
3. 20150116
4. 20150126
5. 201502 ·
6. 20150210
7. 20150211
8. 20150217
9. 201503 ·
10. 20150306
11. 20150326
12. 20150399
13. 201504 ·
14. 20150401
15. 20150410
16. 20150415
17. 20150428
18. 20150499
19. 201505 ·
20. 20150504
21. 20150505
22. 20150507
23. 20150509
24. 20150514
25. 20150519
26. 20150521
27. 20150526
28. 20150528
29. 201506 ·
30. 20150601
31. 20150602
32. 20150604
33. 20150608
34. 20150611
35. 20150612
36. 20150615
37. 20150616
38. 20150618
39. 20150619
40. 20150623
41. 20150625
42. 20150626
43. 20150627
44. 20150699
45. 20150702
46. 20150709
47. 20150727
48. 20150799
49. 201508 ·
50. 20150801
[ 235 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 39 | ( | 90.7% | ) |
|
 |
131
(75.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_hormone
[factor] |
1. 20130499
2. 201501 ·
3. 20150116
4. 20150126
5. 201502 ·
6. 20150210
7. 20150211
8. 20150217
9. 201503 ·
10. 20150306
11. 20150326
12. 20150399
13. 201504 ·
14. 20150401
15. 20150410
16. 20150415
17. 20150428
18. 20150499
19. 201505 ·
20. 20150504
21. 20150505
22. 20150507
23. 20150509
24. 20150514
25. 20150519
26. 20150521
27. 20150526
28. 20150528
29. 201506 ·
30. 20150601
31. 20150602
32. 20150604
33. 20150608
34. 20150611
35. 20150612
36. 20150615
37. 20150616
38. 20150618
39. 20150619
40. 20150623
41. 20150625
42. 20150626
43. 20150627
44. 20150699
45. 20150702
46. 20150709
47. 20150727
48. 20150799
49. 201508 ·
50. 20150801
[ 235 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 62 | ( | 80.5% | ) |
|
 |
213
(73.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_hormone
[factor] |
1. 20130499
2. 201501 ·
3. 20150116
4. 20150126
5. 201502 ·
6. 20150210
7. 20150211
8. 20150217
9. 201503 ·
10. 20150306
11. 20150326
12. 20150399
13. 201504 ·
14. 20150401
15. 20150410
16. 20150415
17. 20150428
18. 20150499
19. 201505 ·
20. 20150504
21. 20150505
22. 20150507
23. 20150509
24. 20150514
25. 20150519
26. 20150521
27. 20150526
28. 20150528
29. 201506 ·
30. 20150601
31. 20150602
32. 20150604
33. 20150608
34. 20150611
35. 20150612
36. 20150615
37. 20150616
38. 20150618
39. 20150619
40. 20150623
41. 20150625
42. 20150626
43. 20150627
44. 20150699
45. 20150702
46. 20150709
47. 20150727
48. 20150799
49. 201508 ·
50. 20150801
[ 235 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.9% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.9% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 0.9% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 191 | ( | 86.0% | ) |
|
 |
865
(79.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_hormone
[factor] |
1. 20130499
2. 201501 ·
3. 20150116
4. 20150126
5. 201502 ·
6. 20150210
7. 20150211
8. 20150217
9. 201503 ·
10. 20150306
11. 20150326
12. 20150399
13. 201504 ·
14. 20150401
15. 20150410
16. 20150415
17. 20150428
18. 20150499
19. 201505 ·
20. 20150504
21. 20150505
22. 20150507
23. 20150509
24. 20150514
25. 20150519
26. 20150521
27. 20150526
28. 20150528
29. 201506 ·
30. 20150601
31. 20150602
32. 20150604
33. 20150608
34. 20150611
35. 20150612
36. 20150615
37. 20150616
38. 20150618
39. 20150619
40. 20150623
41. 20150625
42. 20150626
43. 20150627
44. 20150699
45. 20150702
46. 20150709
47. 20150727
48. 20150799
49. 201508 ·
50. 20150801
[ 235 others ] |
| 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
15
(93.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE BRM
Description: Date of initiation for immunotherapy (a.k.a. biological response modifier) that is part of the first course of treatment. See also RX Summ–BRM [1410]. See Chapter X for date format. Formerly RX Date–BRM.
Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first course of therapy and to reconstruct the sequence of first-course treatment modes. Note: CoC discontinued support of this item from 2003 through 2009.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1240
All data
st_css() #IMPORTANT!
rxdatebrm <- as.factor(d[,"rxdatebrm"])
new.d <- data.frame(new.d, rxdatebrm)
new.d <- apply_labels(new.d, rxdatebrm = "rx_date_brm")
#summary(new.d$rxdatebrm)
temp.d <- data.frame (new.d.1, rxdatebrm)
summarytools::view(dfSummary(new.d$rxdatebrm, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdatebrm
[labelled, factor] |
rx_date_brm |
1. 20160308
2. 20160921
3. 20170130
4. 20170215
5. 201705 · |
| 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) |
|
 |
2162
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_brm
[factor] |
1. 20160308
2. 20160921
3. 20170130
4. 20170215
5. 201705 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_brm
[factor] |
1. 20160308
2. 20160921
3. 20170130
4. 20170215
5. 201705 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_brm
[factor] |
1. 20160308
2. 20160921
3. 20170130
4. 20170215
5. 201705 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) |
|
 |
236
(99.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_brm
[factor] |
1. 20160308
2. 20160921
3. 20170130
4. 20170215
5. 201705 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_brm
[factor] |
1. 20160308
2. 20160921
3. 20170130
4. 20170215
5. 201705 · |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_brm
[factor] |
1. 20160308
2. 20160921
3. 20170130
4. 20170215
5. 201705 · |
| 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1083
(99.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_brm
[factor] |
1. 20160308
2. 20160921
3. 20170130
4. 20170215
5. 201705 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE OTHER
Description: Date of initiation for other treatment that is part of the first course of treatment at any facility. See RX Summ–Other [1420]. See Chapter X for date format. Formerly RX Date–Other.
Rationale: The dates on which different treatment modalities were started are used to evaluate whether the treatments were part of first-course therapy and to reconstruct the sequence of first-course treatment modes.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1250
All data
st_css() #IMPORTANT!
rxdateother <- as.factor(d[,"rxdateother"])
new.d <- data.frame(new.d, rxdateother)
new.d <- apply_labels(new.d, rxdateother = "rx_date_other")
#summary(new.d$rxdateother)
temp.d <- data.frame (new.d.1, rxdateother)
summarytools::view(dfSummary(new.d$rxdateother, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdateother
[labelled, factor] |
rx_date_other |
1. 20150911
2. 201605 ·
3. 20160711 |
|
 |
2164
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_other
[factor] |
1. 20150911
2. 201605 ·
3. 20160711 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_other
[factor] |
1. 20150911
2. 201605 ·
3. 20160711 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_other
[factor] |
1. 20150911
2. 201605 ·
3. 20160711 |
|
 |
236
(99.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_other
[factor] |
1. 20150911
2. 201605 ·
3. 20160711 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_other
[factor] |
1. 20150911
2. 201605 ·
3. 20160711 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_other
[factor] |
1. 20150911
2. 201605 ·
3. 20160711 |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_other
[factor] |
1. 20150911
2. 201605 ·
3. 20160711 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DATE INITIAL RX SEER
Description: Date of initiation of the first course therapy for the tumor being reported, using the SEER definition of first course. See also Date 1st Crs RX CoC [1270]. See Chapter V, Unresolved Issues, for further discussion of the difference between SEER and CoC items. See Chapter X for date format. Use Date Initial RX SEER Flag [1261] if there is no appropriate or known date for this item. Formerly Date of Initial RX–SEER.
Clarification of NPCR Required Status: Central registries funded by NPCR are required to collect either Date Initial RX SEER [1260] or Date 1st Crs RX CoC [1270].
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1260
All data
st_css() #IMPORTANT!
dateinitialrxseer <- as.factor(d[,"dateinitialrxseer"])
new.d <- data.frame(new.d, dateinitialrxseer)
new.d <- apply_labels(new.d, dateinitialrxseer = "date_initial_rx_seer")
#summary(new.d$dateinitialrxseer)
temp.d <- data.frame (new.d.1, dateinitialrxseer)
summarytools::view(dfSummary(new.d$dateinitialrxseer, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
dateinitialrxseer
[labelled, factor] |
date_initial_rx_seer |
1. 20120699
2. 20120999
3. 20130399
4. 20130499
5. 20130699
6. 20131199
7. 20140299
8. 20140599
9. 20141199
10. 2015 ·
11. 201501 ·
12. 20150116
13. 20150126
14. 20150199
15. 201502 ·
16. 20150205
17. 20150210
18. 20150211
19. 20150212
20. 20150217
21. 20150219
22. 20150223
23. 20150224
24. 20150226
25. 201503 ·
26. 20150306
27. 20150310
28. 20150316
29. 20150317
30. 20150323
31. 20150326
32. 20150399
33. 201504 ·
34. 20150401
35. 20150402
36. 20150406
37. 20150407
38. 20150409
39. 20150410
40. 20150413
41. 20150414
42. 20150415
43. 20150416
44. 20150420
45. 20150421
46. 20150423
47. 20150427
48. 20150428
49. 20150429
50. 20150499
[ 552 others ] |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 8 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.3% | ) | | 4 | ( | 0.2% | ) | | 13 | ( | 0.7% | ) | | 6 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 8 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 1727 | ( | 94.1% | ) |
|
 |
332
(15.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_initial_rx_seer
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20130499
5. 20130699
6. 20131199
7. 20140299
8. 20140599
9. 20141199
10. 2015 ·
11. 201501 ·
12. 20150116
13. 20150126
14. 20150199
15. 201502 ·
16. 20150205
17. 20150210
18. 20150211
19. 20150212
20. 20150217
21. 20150219
22. 20150223
23. 20150224
24. 20150226
25. 201503 ·
26. 20150306
27. 20150310
28. 20150316
29. 20150317
30. 20150323
31. 20150326
32. 20150399
33. 201504 ·
34. 20150401
35. 20150402
36. 20150406
37. 20150407
38. 20150409
39. 20150410
40. 20150413
41. 20150414
42. 20150415
43. 20150416
44. 20150420
45. 20150421
46. 20150423
47. 20150427
48. 20150428
49. 20150429
50. 20150499
[ 552 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 3.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 145 | ( | 93.5% | ) |
|
 |
34
(18.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_initial_rx_seer
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20130499
5. 20130699
6. 20131199
7. 20140299
8. 20140599
9. 20141199
10. 2015 ·
11. 201501 ·
12. 20150116
13. 20150126
14. 20150199
15. 201502 ·
16. 20150205
17. 20150210
18. 20150211
19. 20150212
20. 20150217
21. 20150219
22. 20150223
23. 20150224
24. 20150226
25. 201503 ·
26. 20150306
27. 20150310
28. 20150316
29. 20150317
30. 20150323
31. 20150326
32. 20150399
33. 201504 ·
34. 20150401
35. 20150402
36. 20150406
37. 20150407
38. 20150409
39. 20150410
40. 20150413
41. 20150414
42. 20150415
43. 20150416
44. 20150420
45. 20150421
46. 20150423
47. 20150427
48. 20150428
49. 20150429
50. 20150499
[ 552 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 125 | ( | 96.2% | ) |
|
 |
44
(25.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_initial_rx_seer
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20130499
5. 20130699
6. 20131199
7. 20140299
8. 20140599
9. 20141199
10. 2015 ·
11. 201501 ·
12. 20150116
13. 20150126
14. 20150199
15. 201502 ·
16. 20150205
17. 20150210
18. 20150211
19. 20150212
20. 20150217
21. 20150219
22. 20150223
23. 20150224
24. 20150226
25. 201503 ·
26. 20150306
27. 20150310
28. 20150316
29. 20150317
30. 20150323
31. 20150326
32. 20150399
33. 201504 ·
34. 20150401
35. 20150402
36. 20150406
37. 20150407
38. 20150409
39. 20150410
40. 20150413
41. 20150414
42. 20150415
43. 20150416
44. 20150420
45. 20150421
46. 20150423
47. 20150427
48. 20150428
49. 20150429
50. 20150499
[ 552 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 196 | ( | 93.8% | ) |
|
 |
28
(11.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_initial_rx_seer
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20130499
5. 20130699
6. 20131199
7. 20140299
8. 20140599
9. 20141199
10. 2015 ·
11. 201501 ·
12. 20150116
13. 20150126
14. 20150199
15. 201502 ·
16. 20150205
17. 20150210
18. 20150211
19. 20150212
20. 20150217
21. 20150219
22. 20150223
23. 20150224
24. 20150226
25. 201503 ·
26. 20150306
27. 20150310
28. 20150316
29. 20150317
30. 20150323
31. 20150326
32. 20150399
33. 201504 ·
34. 20150401
35. 20150402
36. 20150406
37. 20150407
38. 20150409
39. 20150410
40. 20150413
41. 20150414
42. 20150415
43. 20150416
44. 20150420
45. 20150421
46. 20150423
47. 20150427
48. 20150428
49. 20150429
50. 20150499
[ 552 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.8% | ) | | 133 | ( | 94.3% | ) |
|
 |
33
(19.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_initial_rx_seer
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20130499
5. 20130699
6. 20131199
7. 20140299
8. 20140599
9. 20141199
10. 2015 ·
11. 201501 ·
12. 20150116
13. 20150126
14. 20150199
15. 201502 ·
16. 20150205
17. 20150210
18. 20150211
19. 20150212
20. 20150217
21. 20150219
22. 20150223
23. 20150224
24. 20150226
25. 201503 ·
26. 20150306
27. 20150310
28. 20150316
29. 20150317
30. 20150323
31. 20150326
32. 20150399
33. 201504 ·
34. 20150401
35. 20150402
36. 20150406
37. 20150407
38. 20150409
39. 20150410
40. 20150413
41. 20150414
42. 20150415
43. 20150416
44. 20150420
45. 20150421
46. 20150423
47. 20150427
48. 20150428
49. 20150429
50. 20150499
[ 552 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 4 | ( | 1.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 241 | ( | 91.3% | ) |
|
 |
26
(9.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_initial_rx_seer
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20130499
5. 20130699
6. 20131199
7. 20140299
8. 20140599
9. 20141199
10. 2015 ·
11. 201501 ·
12. 20150116
13. 20150126
14. 20150199
15. 201502 ·
16. 20150205
17. 20150210
18. 20150211
19. 20150212
20. 20150217
21. 20150219
22. 20150223
23. 20150224
24. 20150226
25. 201503 ·
26. 20150306
27. 20150310
28. 20150316
29. 20150317
30. 20150323
31. 20150326
32. 20150399
33. 201504 ·
34. 20150401
35. 20150402
36. 20150406
37. 20150407
38. 20150409
39. 20150410
40. 20150413
41. 20150414
42. 20150415
43. 20150416
44. 20150420
45. 20150421
46. 20150423
47. 20150427
48. 20150428
49. 20150429
50. 20150499
[ 552 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 886 | ( | 95.8% | ) |
|
 |
162
(14.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
date_initial_rx_seer
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20130499
5. 20130699
6. 20131199
7. 20140299
8. 20140599
9. 20141199
10. 2015 ·
11. 201501 ·
12. 20150116
13. 20150126
14. 20150199
15. 201502 ·
16. 20150205
17. 20150210
18. 20150211
19. 20150212
20. 20150217
21. 20150219
22. 20150223
23. 20150224
24. 20150226
25. 201503 ·
26. 20150306
27. 20150310
28. 20150316
29. 20150317
30. 20150323
31. 20150326
32. 20150399
33. 201504 ·
34. 20150401
35. 20150402
36. 20150406
37. 20150407
38. 20150409
39. 20150410
40. 20150413
41. 20150414
42. 20150415
43. 20150416
44. 20150420
45. 20150421
46. 20150423
47. 20150427
48. 20150428
49. 20150429
50. 20150499
[ 552 others ] |
| 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) |
|
 |
5
(31.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE DX/STG PROC
Description: Records the date on which the surgical diagnostic and/or staging procedure was performed. See Surgical and Diagnostic Staging Procedure [1350]. See Chapter X for date format. Formerly RX Date–DX/Stg Proc.
Note: This is a CoC item and for tumors diagnosed from January 1, 1996, through December 31, 2002, this may have been the date on which diagnostic, staging, and palliative procedures were performed. Beginning with tumors diagnosed on or after January 1, 2003, palliative procedures are collected in RX Summ–Palliative Proc [3270] and RX Hosp–Palliative Proc [3280].
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1280
All data
st_css() #IMPORTANT!
rxdatedxstgproc <- as.factor(d[,"rxdatedxstgproc"])
new.d <- data.frame(new.d, rxdatedxstgproc)
new.d <- apply_labels(new.d, rxdatedxstgproc = "rx_date_dxstg_proc")
#summary(new.d$rxdatedxstgproc)
temp.d <- data.frame (new.d.1, rxdatedxstgproc)
summarytools::view(dfSummary(new.d$rxdatedxstgproc, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdatedxstgproc
[labelled, factor] |
rx_date_dxstg_proc |
1. 2015 ·
2. 20150101
3. 20150106
4. 20150112
5. 20150114
6. 20150115
7. 20150116
8. 20150119
9. 20150202
10. 20150210
11. 20150212
12. 20150216
13. 20150218
14. 20150220
15. 201503 ·
16. 20150302
17. 20150303
18. 20150304
19. 20150305
20. 20150310
21. 20150311
22. 20150323
23. 20150326
24. 20150402
25. 20150406
26. 20150407
27. 20150413
28. 20150416
29. 20150417
30. 20150423
31. 20150424
32. 20150511
33. 20150513
34. 20150515
35. 20150519
36. 20150520
37. 20150521
38. 20150526
39. 20150528
40. 20150603
41. 20150604
42. 20150609
43. 20150616
44. 20150617
45. 20150619
46. 20150622
47. 20150623
48. 20150624
49. 20150625
50. 20150629
[ 162 others ] |
| 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 223 | ( | 78.8% | ) |
|
 |
1884
(86.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_dxstg_proc
[factor] |
1. 2015 ·
2. 20150101
3. 20150106
4. 20150112
5. 20150114
6. 20150115
7. 20150116
8. 20150119
9. 20150202
10. 20150210
11. 20150212
12. 20150216
13. 20150218
14. 20150220
15. 201503 ·
16. 20150302
17. 20150303
18. 20150304
19. 20150305
20. 20150310
21. 20150311
22. 20150323
23. 20150326
24. 20150402
25. 20150406
26. 20150407
27. 20150413
28. 20150416
29. 20150417
30. 20150423
31. 20150424
32. 20150511
33. 20150513
34. 20150515
35. 20150519
36. 20150520
37. 20150521
38. 20150526
39. 20150528
40. 20150603
41. 20150604
42. 20150609
43. 20150616
44. 20150617
45. 20150619
46. 20150622
47. 20150623
48. 20150624
49. 20150625
50. 20150629
51. 20150707
52. 20150709
53. 20150710
54. 20150715
55. 20150717
56. 20150721
57. 20150724
58. 20150727
59. 20150730
60. 20150803
61. 20150805
62. 20150810
63. 20150813
64. 20150818
65. 20150824
66. 20150827
67. 20150908
68. 20150909
69. 20150910
70. 20150911
71. 20150917
72. 20150922
73. 20150924
74. 20150930
75. 20151001
76. 20151007
77. 20151019
78. 20151021
79. 20151023
80. 20151030
81. 20151103
82. 20151104
83. 20151105
84. 20151110
85. 20151113
86. 20151117
87. 20151130
88. 20151201
89. 20151202
90. 20151204
91. 20151218
92. 20151228
93. 20151229
94. 20151230
95. 2016 ·
96. 201601 ·
97. 20160104
98. 20160106
99. 20160107
100. 20160115
101. 20160120
102. 20160125
103. 20160126
104. 20160128
105. 20160205
106. 20160208
107. 20160210
108. 20160215
109. 20160224
110. 20160225
111. 20160226
112. 201603 ·
113. 20160310
114. 20160311
115. 20160315
116. 20160318
117. 20160321
118. 20160322
119. 20160324
120. 20160329
121. 20160330
122. 201604 ·
123. 20160401
124. 20160405
125. 20160411
126. 20160412
127. 20160415
128. 20160418
129. 20160419
130. 20160425
131. 20160426
132. 20160429
133. 20160504
134. 20160505
135. 20160509
136. 20160510
137. 20160512
138. 20160516
139. 20160518
140. 20160519
141. 20160520
142. 20160526
143. 20160531
144. 201606 ·
145. 20160602
146. 20160607
147. 20160608
148. 20160610
149. 20160613
150. 20160615
151. 20160616
152. 20160617
153. 20160621
154. 20160622
155. 20160630
156. 20160701
157. 20160705
158. 20160711
159. 20160712
160. 20160714
161. 20160718
162. 20160721
163. 20160726
164. 20160728
165. 20160801
166. 20160802
167. 20160803
168. 20160804
169. 20160805
170. 20160809
171. 20160812
172. 20160817
173. 20160824
174. 20160825
175. 20160831
176. 20160906
177. 20160908
178. 20160909
179. 20160912
180. 20160913
181. 20160916
182. 20160920
183. 20160922
184. 20160923
185. 20160926
186. 20161007
187. 20161010
188. 20161012
189. 20161013
190. 20161017
191. 20161019
192. 20161020
193. 20161021
194. 20161031
195. 20161108
196. 20161109
197. 20161115
198. 20161117
199. 20161122
200. 20161123
201. 20161128
202. 20161130
203. 20161205
204. 20161207
205. 20161208
206. 20161212
207. 20161213
208. 20161214
209. 20161216
210. 20161219
211. 20161221
212. 20161230 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_dxstg_proc
[factor] |
1. 2015 ·
2. 20150101
3. 20150106
4. 20150112
5. 20150114
6. 20150115
7. 20150116
8. 20150119
9. 20150202
10. 20150210
11. 20150212
12. 20150216
13. 20150218
14. 20150220
15. 201503 ·
16. 20150302
17. 20150303
18. 20150304
19. 20150305
20. 20150310
21. 20150311
22. 20150323
23. 20150326
24. 20150402
25. 20150406
26. 20150407
27. 20150413
28. 20150416
29. 20150417
30. 20150423
31. 20150424
32. 20150511
33. 20150513
34. 20150515
35. 20150519
36. 20150520
37. 20150521
38. 20150526
39. 20150528
40. 20150603
41. 20150604
42. 20150609
43. 20150616
44. 20150617
45. 20150619
46. 20150622
47. 20150623
48. 20150624
49. 20150625
50. 20150629
51. 20150707
52. 20150709
53. 20150710
54. 20150715
55. 20150717
56. 20150721
57. 20150724
58. 20150727
59. 20150730
60. 20150803
61. 20150805
62. 20150810
63. 20150813
64. 20150818
65. 20150824
66. 20150827
67. 20150908
68. 20150909
69. 20150910
70. 20150911
71. 20150917
72. 20150922
73. 20150924
74. 20150930
75. 20151001
76. 20151007
77. 20151019
78. 20151021
79. 20151023
80. 20151030
81. 20151103
82. 20151104
83. 20151105
84. 20151110
85. 20151113
86. 20151117
87. 20151130
88. 20151201
89. 20151202
90. 20151204
91. 20151218
92. 20151228
93. 20151229
94. 20151230
95. 2016 ·
96. 201601 ·
97. 20160104
98. 20160106
99. 20160107
100. 20160115
101. 20160120
102. 20160125
103. 20160126
104. 20160128
105. 20160205
106. 20160208
107. 20160210
108. 20160215
109. 20160224
110. 20160225
111. 20160226
112. 201603 ·
113. 20160310
114. 20160311
115. 20160315
116. 20160318
117. 20160321
118. 20160322
119. 20160324
120. 20160329
121. 20160330
122. 201604 ·
123. 20160401
124. 20160405
125. 20160411
126. 20160412
127. 20160415
128. 20160418
129. 20160419
130. 20160425
131. 20160426
132. 20160429
133. 20160504
134. 20160505
135. 20160509
136. 20160510
137. 20160512
138. 20160516
139. 20160518
140. 20160519
141. 20160520
142. 20160526
143. 20160531
144. 201606 ·
145. 20160602
146. 20160607
147. 20160608
148. 20160610
149. 20160613
150. 20160615
151. 20160616
152. 20160617
153. 20160621
154. 20160622
155. 20160630
156. 20160701
157. 20160705
158. 20160711
159. 20160712
160. 20160714
161. 20160718
162. 20160721
163. 20160726
164. 20160728
165. 20160801
166. 20160802
167. 20160803
168. 20160804
169. 20160805
170. 20160809
171. 20160812
172. 20160817
173. 20160824
174. 20160825
175. 20160831
176. 20160906
177. 20160908
178. 20160909
179. 20160912
180. 20160913
181. 20160916
182. 20160920
183. 20160922
184. 20160923
185. 20160926
186. 20161007
187. 20161010
188. 20161012
189. 20161013
190. 20161017
191. 20161019
192. 20161020
193. 20161021
194. 20161031
195. 20161108
196. 20161109
197. 20161115
198. 20161117
199. 20161122
200. 20161123
201. 20161128
202. 20161130
203. 20161205
204. 20161207
205. 20161208
206. 20161212
207. 20161213
208. 20161214
209. 20161216
210. 20161219
211. 20161221
212. 20161230 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_dxstg_proc
[factor] |
1. 2015 ·
2. 20150101
3. 20150106
4. 20150112
5. 20150114
6. 20150115
7. 20150116
8. 20150119
9. 20150202
10. 20150210
11. 20150212
12. 20150216
13. 20150218
14. 20150220
15. 201503 ·
16. 20150302
17. 20150303
18. 20150304
19. 20150305
20. 20150310
21. 20150311
22. 20150323
23. 20150326
24. 20150402
25. 20150406
26. 20150407
27. 20150413
28. 20150416
29. 20150417
30. 20150423
31. 20150424
32. 20150511
33. 20150513
34. 20150515
35. 20150519
36. 20150520
37. 20150521
38. 20150526
39. 20150528
40. 20150603
41. 20150604
42. 20150609
43. 20150616
44. 20150617
45. 20150619
46. 20150622
47. 20150623
48. 20150624
49. 20150625
50. 20150629
51. 20150707
52. 20150709
53. 20150710
54. 20150715
55. 20150717
56. 20150721
57. 20150724
58. 20150727
59. 20150730
60. 20150803
61. 20150805
62. 20150810
63. 20150813
64. 20150818
65. 20150824
66. 20150827
67. 20150908
68. 20150909
69. 20150910
70. 20150911
71. 20150917
72. 20150922
73. 20150924
74. 20150930
75. 20151001
76. 20151007
77. 20151019
78. 20151021
79. 20151023
80. 20151030
81. 20151103
82. 20151104
83. 20151105
84. 20151110
85. 20151113
86. 20151117
87. 20151130
88. 20151201
89. 20151202
90. 20151204
91. 20151218
92. 20151228
93. 20151229
94. 20151230
95. 2016 ·
96. 201601 ·
97. 20160104
98. 20160106
99. 20160107
100. 20160115
101. 20160120
102. 20160125
103. 20160126
104. 20160128
105. 20160205
106. 20160208
107. 20160210
108. 20160215
109. 20160224
110. 20160225
111. 20160226
112. 201603 ·
113. 20160310
114. 20160311
115. 20160315
116. 20160318
117. 20160321
118. 20160322
119. 20160324
120. 20160329
121. 20160330
122. 201604 ·
123. 20160401
124. 20160405
125. 20160411
126. 20160412
127. 20160415
128. 20160418
129. 20160419
130. 20160425
131. 20160426
132. 20160429
133. 20160504
134. 20160505
135. 20160509
136. 20160510
137. 20160512
138. 20160516
139. 20160518
140. 20160519
141. 20160520
142. 20160526
143. 20160531
144. 201606 ·
145. 20160602
146. 20160607
147. 20160608
148. 20160610
149. 20160613
150. 20160615
151. 20160616
152. 20160617
153. 20160621
154. 20160622
155. 20160630
156. 20160701
157. 20160705
158. 20160711
159. 20160712
160. 20160714
161. 20160718
162. 20160721
163. 20160726
164. 20160728
165. 20160801
166. 20160802
167. 20160803
168. 20160804
169. 20160805
170. 20160809
171. 20160812
172. 20160817
173. 20160824
174. 20160825
175. 20160831
176. 20160906
177. 20160908
178. 20160909
179. 20160912
180. 20160913
181. 20160916
182. 20160920
183. 20160922
184. 20160923
185. 20160926
186. 20161007
187. 20161010
188. 20161012
189. 20161013
190. 20161017
191. 20161019
192. 20161020
193. 20161021
194. 20161031
195. 20161108
196. 20161109
197. 20161115
198. 20161117
199. 20161122
200. 20161123
201. 20161128
202. 20161130
203. 20161205
204. 20161207
205. 20161208
206. 20161212
207. 20161213
208. 20161214
209. 20161216
210. 20161219
211. 20161221
212. 20161230 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_dxstg_proc
[factor] |
1. 2015 ·
2. 20150101
3. 20150106
4. 20150112
5. 20150114
6. 20150115
7. 20150116
8. 20150119
9. 20150202
10. 20150210
11. 20150212
12. 20150216
13. 20150218
14. 20150220
15. 201503 ·
16. 20150302
17. 20150303
18. 20150304
19. 20150305
20. 20150310
21. 20150311
22. 20150323
23. 20150326
24. 20150402
25. 20150406
26. 20150407
27. 20150413
28. 20150416
29. 20150417
30. 20150423
31. 20150424
32. 20150511
33. 20150513
34. 20150515
35. 20150519
36. 20150520
37. 20150521
38. 20150526
39. 20150528
40. 20150603
41. 20150604
42. 20150609
43. 20150616
44. 20150617
45. 20150619
46. 20150622
47. 20150623
48. 20150624
49. 20150625
50. 20150629
51. 20150707
52. 20150709
53. 20150710
54. 20150715
55. 20150717
56. 20150721
57. 20150724
58. 20150727
59. 20150730
60. 20150803
61. 20150805
62. 20150810
63. 20150813
64. 20150818
65. 20150824
66. 20150827
67. 20150908
68. 20150909
69. 20150910
70. 20150911
71. 20150917
72. 20150922
73. 20150924
74. 20150930
75. 20151001
76. 20151007
77. 20151019
78. 20151021
79. 20151023
80. 20151030
81. 20151103
82. 20151104
83. 20151105
84. 20151110
85. 20151113
86. 20151117
87. 20151130
88. 20151201
89. 20151202
90. 20151204
91. 20151218
92. 20151228
93. 20151229
94. 20151230
95. 2016 ·
96. 201601 ·
97. 20160104
98. 20160106
99. 20160107
100. 20160115
101. 20160120
102. 20160125
103. 20160126
104. 20160128
105. 20160205
106. 20160208
107. 20160210
108. 20160215
109. 20160224
110. 20160225
111. 20160226
112. 201603 ·
113. 20160310
114. 20160311
115. 20160315
116. 20160318
117. 20160321
118. 20160322
119. 20160324
120. 20160329
121. 20160330
122. 201604 ·
123. 20160401
124. 20160405
125. 20160411
126. 20160412
127. 20160415
128. 20160418
129. 20160419
130. 20160425
131. 20160426
132. 20160429
133. 20160504
134. 20160505
135. 20160509
136. 20160510
137. 20160512
138. 20160516
139. 20160518
140. 20160519
141. 20160520
142. 20160526
143. 20160531
144. 201606 ·
145. 20160602
146. 20160607
147. 20160608
148. 20160610
149. 20160613
150. 20160615
151. 20160616
152. 20160617
153. 20160621
154. 20160622
155. 20160630
156. 20160701
157. 20160705
158. 20160711
159. 20160712
160. 20160714
161. 20160718
162. 20160721
163. 20160726
164. 20160728
165. 20160801
166. 20160802
167. 20160803
168. 20160804
169. 20160805
170. 20160809
171. 20160812
172. 20160817
173. 20160824
174. 20160825
175. 20160831
176. 20160906
177. 20160908
178. 20160909
179. 20160912
180. 20160913
181. 20160916
182. 20160920
183. 20160922
184. 20160923
185. 20160926
186. 20161007
187. 20161010
188. 20161012
189. 20161013
190. 20161017
191. 20161019
192. 20161020
193. 20161021
194. 20161031
195. 20161108
196. 20161109
197. 20161115
198. 20161117
199. 20161122
200. 20161123
201. 20161128
202. 20161130
203. 20161205
204. 20161207
205. 20161208
206. 20161212
207. 20161213
208. 20161214
209. 20161216
210. 20161219
211. 20161221
212. 20161230 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_dxstg_proc
[factor] |
1. 2015 ·
2. 20150101
3. 20150106
4. 20150112
5. 20150114
6. 20150115
7. 20150116
8. 20150119
9. 20150202
10. 20150210
11. 20150212
12. 20150216
13. 20150218
14. 20150220
15. 201503 ·
16. 20150302
17. 20150303
18. 20150304
19. 20150305
20. 20150310
21. 20150311
22. 20150323
23. 20150326
24. 20150402
25. 20150406
26. 20150407
27. 20150413
28. 20150416
29. 20150417
30. 20150423
31. 20150424
32. 20150511
33. 20150513
34. 20150515
35. 20150519
36. 20150520
37. 20150521
38. 20150526
39. 20150528
40. 20150603
41. 20150604
42. 20150609
43. 20150616
44. 20150617
45. 20150619
46. 20150622
47. 20150623
48. 20150624
49. 20150625
50. 20150629
[ 162 others ] |
| 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.7% | ) | | 223 | ( | 78.8% | ) |
|
 |
7
(2.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_dxstg_proc
[factor] |
1. 2015 ·
2. 20150101
3. 20150106
4. 20150112
5. 20150114
6. 20150115
7. 20150116
8. 20150119
9. 20150202
10. 20150210
11. 20150212
12. 20150216
13. 20150218
14. 20150220
15. 201503 ·
16. 20150302
17. 20150303
18. 20150304
19. 20150305
20. 20150310
21. 20150311
22. 20150323
23. 20150326
24. 20150402
25. 20150406
26. 20150407
27. 20150413
28. 20150416
29. 20150417
30. 20150423
31. 20150424
32. 20150511
33. 20150513
34. 20150515
35. 20150519
36. 20150520
37. 20150521
38. 20150526
39. 20150528
40. 20150603
41. 20150604
42. 20150609
43. 20150616
44. 20150617
45. 20150619
46. 20150622
47. 20150623
48. 20150624
49. 20150625
50. 20150629
51. 20150707
52. 20150709
53. 20150710
54. 20150715
55. 20150717
56. 20150721
57. 20150724
58. 20150727
59. 20150730
60. 20150803
61. 20150805
62. 20150810
63. 20150813
64. 20150818
65. 20150824
66. 20150827
67. 20150908
68. 20150909
69. 20150910
70. 20150911
71. 20150917
72. 20150922
73. 20150924
74. 20150930
75. 20151001
76. 20151007
77. 20151019
78. 20151021
79. 20151023
80. 20151030
81. 20151103
82. 20151104
83. 20151105
84. 20151110
85. 20151113
86. 20151117
87. 20151130
88. 20151201
89. 20151202
90. 20151204
91. 20151218
92. 20151228
93. 20151229
94. 20151230
95. 2016 ·
96. 201601 ·
97. 20160104
98. 20160106
99. 20160107
100. 20160115
101. 20160120
102. 20160125
103. 20160126
104. 20160128
105. 20160205
106. 20160208
107. 20160210
108. 20160215
109. 20160224
110. 20160225
111. 20160226
112. 201603 ·
113. 20160310
114. 20160311
115. 20160315
116. 20160318
117. 20160321
118. 20160322
119. 20160324
120. 20160329
121. 20160330
122. 201604 ·
123. 20160401
124. 20160405
125. 20160411
126. 20160412
127. 20160415
128. 20160418
129. 20160419
130. 20160425
131. 20160426
132. 20160429
133. 20160504
134. 20160505
135. 20160509
136. 20160510
137. 20160512
138. 20160516
139. 20160518
140. 20160519
141. 20160520
142. 20160526
143. 20160531
144. 201606 ·
145. 20160602
146. 20160607
147. 20160608
148. 20160610
149. 20160613
150. 20160615
151. 20160616
152. 20160617
153. 20160621
154. 20160622
155. 20160630
156. 20160701
157. 20160705
158. 20160711
159. 20160712
160. 20160714
161. 20160718
162. 20160721
163. 20160726
164. 20160728
165. 20160801
166. 20160802
167. 20160803
168. 20160804
169. 20160805
170. 20160809
171. 20160812
172. 20160817
173. 20160824
174. 20160825
175. 20160831
176. 20160906
177. 20160908
178. 20160909
179. 20160912
180. 20160913
181. 20160916
182. 20160920
183. 20160922
184. 20160923
185. 20160926
186. 20161007
187. 20161010
188. 20161012
189. 20161013
190. 20161017
191. 20161019
192. 20161020
193. 20161021
194. 20161031
195. 20161108
196. 20161109
197. 20161115
198. 20161117
199. 20161122
200. 20161123
201. 20161128
202. 20161130
203. 20161205
204. 20161207
205. 20161208
206. 20161212
207. 20161213
208. 20161214
209. 20161216
210. 20161219
211. 20161221
212. 20161230 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_dxstg_proc
[factor] |
1. 2015 ·
2. 20150101
3. 20150106
4. 20150112
5. 20150114
6. 20150115
7. 20150116
8. 20150119
9. 20150202
10. 20150210
11. 20150212
12. 20150216
13. 20150218
14. 20150220
15. 201503 ·
16. 20150302
17. 20150303
18. 20150304
19. 20150305
20. 20150310
21. 20150311
22. 20150323
23. 20150326
24. 20150402
25. 20150406
26. 20150407
27. 20150413
28. 20150416
29. 20150417
30. 20150423
31. 20150424
32. 20150511
33. 20150513
34. 20150515
35. 20150519
36. 20150520
37. 20150521
38. 20150526
39. 20150528
40. 20150603
41. 20150604
42. 20150609
43. 20150616
44. 20150617
45. 20150619
46. 20150622
47. 20150623
48. 20150624
49. 20150625
50. 20150629
51. 20150707
52. 20150709
53. 20150710
54. 20150715
55. 20150717
56. 20150721
57. 20150724
58. 20150727
59. 20150730
60. 20150803
61. 20150805
62. 20150810
63. 20150813
64. 20150818
65. 20150824
66. 20150827
67. 20150908
68. 20150909
69. 20150910
70. 20150911
71. 20150917
72. 20150922
73. 20150924
74. 20150930
75. 20151001
76. 20151007
77. 20151019
78. 20151021
79. 20151023
80. 20151030
81. 20151103
82. 20151104
83. 20151105
84. 20151110
85. 20151113
86. 20151117
87. 20151130
88. 20151201
89. 20151202
90. 20151204
91. 20151218
92. 20151228
93. 20151229
94. 20151230
95. 2016 ·
96. 201601 ·
97. 20160104
98. 20160106
99. 20160107
100. 20160115
101. 20160120
102. 20160125
103. 20160126
104. 20160128
105. 20160205
106. 20160208
107. 20160210
108. 20160215
109. 20160224
110. 20160225
111. 20160226
112. 201603 ·
113. 20160310
114. 20160311
115. 20160315
116. 20160318
117. 20160321
118. 20160322
119. 20160324
120. 20160329
121. 20160330
122. 201604 ·
123. 20160401
124. 20160405
125. 20160411
126. 20160412
127. 20160415
128. 20160418
129. 20160419
130. 20160425
131. 20160426
132. 20160429
133. 20160504
134. 20160505
135. 20160509
136. 20160510
137. 20160512
138. 20160516
139. 20160518
140. 20160519
141. 20160520
142. 20160526
143. 20160531
144. 201606 ·
145. 20160602
146. 20160607
147. 20160608
148. 20160610
149. 20160613
150. 20160615
151. 20160616
152. 20160617
153. 20160621
154. 20160622
155. 20160630
156. 20160701
157. 20160705
158. 20160711
159. 20160712
160. 20160714
161. 20160718
162. 20160721
163. 20160726
164. 20160728
165. 20160801
166. 20160802
167. 20160803
168. 20160804
169. 20160805
170. 20160809
171. 20160812
172. 20160817
173. 20160824
174. 20160825
175. 20160831
176. 20160906
177. 20160908
178. 20160909
179. 20160912
180. 20160913
181. 20160916
182. 20160920
183. 20160922
184. 20160923
185. 20160926
186. 20161007
187. 20161010
188. 20161012
189. 20161013
190. 20161017
191. 20161019
192. 20161020
193. 20161021
194. 20161031
195. 20161108
196. 20161109
197. 20161115
198. 20161117
199. 20161122
200. 20161123
201. 20161128
202. 20161130
203. 20161205
204. 20161207
205. 20161208
206. 20161212
207. 20161213
208. 20161214
209. 20161216
210. 20161219
211. 20161221
212. 20161230 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
# RX SUMM–TREATMENT STATUS {.tabset} - Description: This data item is a summary of the status for all treatment modalities. It is used in conjunction with Date Initial RX SEER [1260] and/or Date 1st Crs RX CoC [1270] and each modality of treatment with their respective date field to document whether treatment was given or not given, whether it is unknown if treatment was given, or whether treatment was given on an unknown date. Also indicates active surveillance (watchful waiting). This data item is effective for January 2010+ diagnoses.
Rationale: This field will document active surveillance (watchful waiting) and eliminate searching each treatment modality to determine whether treatment was given.
Codes
- 0 No treatment given
- 1 Treatment given
- 2 Active surveillance (watchful waiting)
- 9 Unknown if treatment was given
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1285
All data
st_css() #IMPORTANT!
rxsummtreatmentstatus <- as.factor(trimws(d[,"rxsummtreatmentstatus"]))
levels(rxsummtreatmentstatus) <- list( No_treatment_given.0="0",
Treatment_given.1="1",
Active.2="2",
Unknown.9="9"
)
rxsummtreatmentstatus <- relevel(rxsummtreatmentstatus, ref="Treatment_given.1")
new.d <- data.frame(new.d, rxsummtreatmentstatus)
new.d <- apply_labels(new.d, rxsummtreatmentstatus = "Status for all treatment modalities")
#summary(new.d$rxsummtreatmentstatus)
temp.d <- data.frame (new.d.1, rxsummtreatmentstatus)
summarytools::view(dfSummary(new.d$rxsummtreatmentstatus, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummtreatmentstatus
[labelled, factor] |
Status for all treatment modalities |
1. Treatment_given.1
2. No_treatment_given.0
3. Active.2
4. Unknown.9 |
| 1829 | ( | 84.4% | ) | | 207 | ( | 9.6% | ) | | 117 | ( | 5.4% | ) | | 14 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_treatment_status
[factor] |
1. Treatment_given.1
2. No_treatment_given.0
3. Active.2
4. Unknown.9 |
| 154 | ( | 81.5% | ) | | 15 | ( | 7.9% | ) | | 15 | ( | 7.9% | ) | | 5 | ( | 2.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_treatment_status
[factor] |
1. Treatment_given.1
2. No_treatment_given.0
3. Active.2
4. Unknown.9 |
| 126 | ( | 72.4% | ) | | 12 | ( | 6.9% | ) | | 34 | ( | 19.5% | ) | | 2 | ( | 1.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_treatment_status
[factor] |
1. Treatment_given.1
2. No_treatment_given.0
3. Active.2
4. Unknown.9 |
| 205 | ( | 86.5% | ) | | 12 | ( | 5.1% | ) | | 17 | ( | 7.2% | ) | | 3 | ( | 1.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_treatment_status
[factor] |
1. Treatment_given.1
2. No_treatment_given.0
3. Active.2
4. Unknown.9 |
| 141 | ( | 81.0% | ) | | 19 | ( | 10.9% | ) | | 14 | ( | 8.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_treatment_status
[factor] |
1. Treatment_given.1
2. No_treatment_given.0
3. Active.2
4. Unknown.9 |
| 264 | ( | 91.0% | ) | | 16 | ( | 5.5% | ) | | 6 | ( | 2.1% | ) | | 4 | ( | 1.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_treatment_status
[factor] |
1. Treatment_given.1
2. No_treatment_given.0
3. Active.2
4. Unknown.9 |
| 928 | ( | 85.4% | ) | | 130 | ( | 12.0% | ) | | 29 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_treatment_status
[factor] |
1. Treatment_given.1
2. No_treatment_given.0
3. Active.2
4. Unknown.9 |
| 11 | ( | 68.8% | ) | | 3 | ( | 18.8% | ) | | 2 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–SURG PRIM SITE
Description: Site-specific codes for the type of surgery to the primary site performed as part of the first course of treatment. This includes treatment given at all facilities as part of the first course of treatment.
Codes: (in addition to the site-specific codes; Refer to the most recent version of STORE and SEER Program Code manual for additional instructions.)
- 00 None
- 10-19 Site-specific code; tumor destruction
- 20-80 Site-specific codes; resection
- 90 Surgery, NOS
- 98 Site specific codes; special
- 99 Unknown
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1290
All data
st_css() #IMPORTANT!
# Put 10-19 into one catecory and 20-80 into one category
a<-as.numeric(d$rxsummsurgprimsite)
a[which(a>=10&a<=19)]<-10
a[which(a>=20&a<=80)]<-20
rxsummsurgprimsite <- as.factor(a)
levels(rxsummsurgprimsite) <- list(None.0="0",
Tumor_destruction.10="10",
Resection.20="20",
Surgery_NOS.90="90",
Unknown.99="99"
)
new.d <- data.frame(new.d, rxsummsurgprimsite)
new.d <- apply_labels(new.d, rxsummsurgprimsite = "Type of radiation therapy")
#summary(new.d$rxsummsurgprimsite)
temp.d <- data.frame (new.d.1, rxsummsurgprimsite)
summarytools::view(dfSummary(new.d$rxsummsurgprimsite, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummsurgprimsite
[labelled, factor] |
Type of radiation therapy |
1. None.0
2. Tumor_destruction.10
3. Resection.20
4. Surgery_NOS.90
5. Unknown.99 |
| 1148 | ( | 53.0% | ) | | 12 | ( | 0.6% | ) | | 1002 | ( | 46.2% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_prim_site
[factor] |
1. None.0
2. Tumor_destruction.10
3. Resection.20
4. Surgery_NOS.90
5. Unknown.99 |
| 84 | ( | 44.4% | ) | | 1 | ( | 0.5% | ) | | 104 | ( | 55.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_prim_site
[factor] |
1. None.0
2. Tumor_destruction.10
3. Resection.20
4. Surgery_NOS.90
5. Unknown.99 |
| 126 | ( | 72.4% | ) | | 0 | ( | 0.0% | ) | | 48 | ( | 27.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_prim_site
[factor] |
1. None.0
2. Tumor_destruction.10
3. Resection.20
4. Surgery_NOS.90
5. Unknown.99 |
| 92 | ( | 38.8% | ) | | 1 | ( | 0.4% | ) | | 143 | ( | 60.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_prim_site
[factor] |
1. None.0
2. Tumor_destruction.10
3. Resection.20
4. Surgery_NOS.90
5. Unknown.99 |
| 89 | ( | 51.1% | ) | | 1 | ( | 0.6% | ) | | 84 | ( | 48.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_prim_site
[factor] |
1. None.0
2. Tumor_destruction.10
3. Resection.20
4. Surgery_NOS.90
5. Unknown.99 |
| 104 | ( | 35.9% | ) | | 1 | ( | 0.3% | ) | | 181 | ( | 62.4% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_prim_site
[factor] |
1. None.0
2. Tumor_destruction.10
3. Resection.20
4. Surgery_NOS.90
5. Unknown.99 |
| 644 | ( | 59.2% | ) | | 7 | ( | 0.6% | ) | | 436 | ( | 40.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_prim_site
[factor] |
1. None.0
2. Tumor_destruction.10
3. Resection.20
4. Surgery_NOS.90
5. Unknown.99 |
| 9 | ( | 56.2% | ) | | 1 | ( | 6.2% | ) | | 6 | ( | 37.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–SCOPE REG LN SUR
Description: Describes the removal, biopsy or aspiration of regional lymph node(s) at the time of surgery of the primary site or during a separate surgical event at all facilities.
Rationale: In evaluating quality-of-care and treatment practices it is important to identify the removal, biopsy, or aspiration of regional lymph node(s) at the time of surgery of the primary site or during a separate surgical event.
Codes (Refer to the most recent versions of STORE and the SEER Program Code Manual for instructions that should be applied to all surgically treated cases for all types of cancers.) The treatment of breast and skin cancers are where the distinction between sentinel lymph node biopsies (SLNBx) and more extensive dissection of regional lymph nodes is most frequently encountered. For all other sites, non-sentinel regional node dissections are typical, and codes 2, 6 and 7 are infrequently used.
- 0 None
- 1 Biopsy or aspiration of regional lymph node, NOS
- 2 Sentinel lymph node biopsy
- 3 Number of regional lymph nodes removed unknown, not stated; regional lymph nodes removed, NOS
- 4 1 to 3 regional lymph nodes removed
- 5 4 or more regional lymph nodes removed
- 6 Sentinel node biopsy and code 3, 4, or 5 at same time or timing not noted
- 7 Sentinel node biopsy and code 3, 4, or 5 at different times
- 9 Unknown or not applicable
Note: One important use of registry data is the tracking of treatment patterns over time. To compare contemporary treatment to previously published treatment based on former codes, or to data unmodified from pre-1998 definitions, the ability to differentiate surgeries in which four or more regional lymph nodes are removed is desirable. However, it is very important to note that the distinction between codes 4 and 5 is made to permit comparison of current surgical procedures with procedures coded in the past when the removal of fewer than 4 nodes was not reflected in surgery codes. It is not intended to reflect clinical significance when applied to a particular surgical procedure. It is important to avoid inferring, by data presentation or other methods, that one category is preferable to another within the intent of these items.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1292
All data
st_css() #IMPORTANT!
# Put 10-19 into one catecory and 20-80 into one category
rxsummscopereglnsur <- as.factor(trimws(d[,"rxsummscopereglnsur"]))
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="0"] <- "None.0"
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="1"] <- "Biopsy_or_aspiration_nodes.1"
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="2"] <- "Sentinel_biopsy.2"
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="3"] <- "Number_of_removed_unknown.3"
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="4"] <- "1_3_removed.4"
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="5"] <- "4_more_removed.5"
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="6"] <- "code_345_same_time.6"
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="7"] <- "code_345_diff_time.7"
levels(rxsummscopereglnsur)[levels(rxsummscopereglnsur)=="9"] <- "Unknown.9"
new.d <- data.frame(new.d, rxsummscopereglnsur)
new.d <- apply_labels(new.d, rxsummscopereglnsur = "rx_summ_scope_reg_ln_sur")
#summary(new.d$rxsummsurgprimsite)
temp.d <- data.frame (new.d.1, rxsummscopereglnsur)
summarytools::view(dfSummary(new.d$rxsummscopereglnsur, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummscopereglnsur
[labelled, factor] |
rx_summ_scope_reg_ln_sur |
1. None.0
2. Biopsy_or_aspiration_node
3. Sentinel_biopsy.2
4. Number_of_removed_unknown
5. 1_3_removed.4
6. 4_more_removed.5
7. code_345_same_time.6
8. Unknown.9 |
| 1460 | ( | 67.4% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 8 | ( | 0.4% | ) | | 171 | ( | 7.9% | ) | | 517 | ( | 23.9% | ) | | 2 | ( | 0.1% | ) | | 5 | ( | 0.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_scope_reg_ln_sur
[factor] |
1. None.0
2. Biopsy_or_aspiration_node
3. Sentinel_biopsy.2
4. Number_of_removed_unknown
5. 1_3_removed.4
6. 4_more_removed.5
7. code_345_same_time.6
8. Unknown.9 |
| 124 | ( | 65.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 4.8% | ) | | 56 | ( | 29.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_scope_reg_ln_sur
[factor] |
1. None.0
2. Biopsy_or_aspiration_node
3. Sentinel_biopsy.2
4. Number_of_removed_unknown
5. 1_3_removed.4
6. 4_more_removed.5
7. code_345_same_time.6
8. Unknown.9 |
| 137 | ( | 78.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 4.0% | ) | | 30 | ( | 17.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_scope_reg_ln_sur
[factor] |
1. None.0
2. Biopsy_or_aspiration_node
3. Sentinel_biopsy.2
4. Number_of_removed_unknown
5. 1_3_removed.4
6. 4_more_removed.5
7. code_345_same_time.6
8. Unknown.9 |
| 131 | ( | 55.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 24 | ( | 10.1% | ) | | 80 | ( | 33.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_scope_reg_ln_sur
[factor] |
1. None.0
2. Biopsy_or_aspiration_node
3. Sentinel_biopsy.2
4. Number_of_removed_unknown
5. 1_3_removed.4
6. 4_more_removed.5
7. code_345_same_time.6
8. Unknown.9 |
| 103 | ( | 59.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 10 | ( | 5.7% | ) | | 58 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_scope_reg_ln_sur
[factor] |
1. None.0
2. Biopsy_or_aspiration_node
3. Sentinel_biopsy.2
4. Number_of_removed_unknown
5. 1_3_removed.4
6. 4_more_removed.5
7. code_345_same_time.6
8. Unknown.9 |
| 121 | ( | 41.7% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 56 | ( | 19.3% | ) | | 106 | ( | 36.6% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_scope_reg_ln_sur
[factor] |
1. None.0
2. Biopsy_or_aspiration_node
3. Sentinel_biopsy.2
4. Number_of_removed_unknown
5. 1_3_removed.4
6. 4_more_removed.5
7. code_345_same_time.6
8. Unknown.9 |
| 833 | ( | 76.6% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 63 | ( | 5.8% | ) | | 184 | ( | 16.9% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_scope_reg_ln_sur
[factor] |
1. None.0
2. Biopsy_or_aspiration_node
3. Sentinel_biopsy.2
4. Number_of_removed_unknown
5. 1_3_removed.4
6. 4_more_removed.5
7. code_345_same_time.6
8. Unknown.9 |
| 11 | ( | 68.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 12.5% | ) | | 3 | ( | 18.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–SURG OTH REG/DIS
- Description: Records the surgical removal of distant lymph nodes or other tissue(s)/organ(s) beyond the primary site.
- Rationale: The removal of non-primary tissue documents the extent of surgical treatment and is useful in evaluating the extent of metastatic involvement.
- Codes (Refer to the most recent version of STORE and SEER Program Code Manual for additional instructions.)
- 0 None; diagnosed at autopsy
- 1 Non-primary surgical procedure performed
- 2 Non-primary surgical procedure to other regional sites
- 3 Non-primary surgical procedure to distant lymph node(s)
- 4 Non-primary surgical procedure to distant site
- 5 Any combination of codes 2, 3, or 4
- 9 Unknown; death certificate only
- Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1294
All data
st_css() #IMPORTANT!
# Put 10-19 into one catecory and 20-80 into one category
rxsummsurgothregdis <- as.factor(trimws(d[,"rxsummsurgothregdis"]))
levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="0"] <- "None.0"
levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="1"] <- "Procedure_performed.1"
levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="2"] <- "Other_regional_sites.2"
levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="3"] <- "Distant_lymph_node.3"
levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="4"] <- "Distant_site.4"
levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="5"] <- "Combination_of_234.5"
levels(rxsummsurgothregdis)[levels(rxsummsurgothregdis)=="9"] <- "Unknown.9"
new.d <- data.frame(new.d, rxsummsurgothregdis)
new.d <- apply_labels(new.d, rxsummsurgothregdis = "rx_summ_surg_oth_reg_dis")
#summary(new.d$rxsummsurgprimsite)
temp.d <- data.frame (new.d.1, rxsummsurgothregdis)
summarytools::view(dfSummary(new.d$rxsummsurgothregdis, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummsurgothregdis
[labelled, factor] |
rx_summ_surg_oth_reg_dis |
1. None.0
2. Procedure_performed.1
3. Other_regional_sites.2
4. Distant_lymph_node.3
5. Combination_of_234.5
6. Unknown.9 |
| 2145 | ( | 99.0% | ) | | 6 | ( | 0.3% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_oth_reg_dis
[factor] |
1. None.0
2. Procedure_performed.1
3. Other_regional_sites.2
4. Distant_lymph_node.3
5. Combination_of_234.5
6. Unknown.9 |
| 188 | ( | 99.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_oth_reg_dis
[factor] |
1. None.0
2. Procedure_performed.1
3. Other_regional_sites.2
4. Distant_lymph_node.3
5. Combination_of_234.5
6. Unknown.9 |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_oth_reg_dis
[factor] |
1. None.0
2. Procedure_performed.1
3. Other_regional_sites.2
4. Distant_lymph_node.3
5. Combination_of_234.5
6. Unknown.9 |
| 234 | ( | 98.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_oth_reg_dis
[factor] |
1. None.0
2. Procedure_performed.1
3. Other_regional_sites.2
4. Distant_lymph_node.3
5. Combination_of_234.5
6. Unknown.9 |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_oth_reg_dis
[factor] |
1. None.0
2. Procedure_performed.1
3. Other_regional_sites.2
4. Distant_lymph_node.3
5. Combination_of_234.5
6. Unknown.9 |
| 283 | ( | 97.6% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 5 | ( | 1.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_oth_reg_dis
[factor] |
1. None.0
2. Procedure_performed.1
3. Other_regional_sites.2
4. Distant_lymph_node.3
5. Combination_of_234.5
6. Unknown.9 |
| 1077 | ( | 99.1% | ) | | 5 | ( | 0.5% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_oth_reg_dis
[factor] |
1. None.0
2. Procedure_performed.1
3. Other_regional_sites.2
4. Distant_lymph_node.3
5. Combination_of_234.5
6. Unknown.9 |
| 15 | ( | 93.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
REASON FOR NO SURGERY
- Description: Records the reason that no surgery was performed on the primary site.
- Rationale: This data item provides information related to the quality of care and describes why primary site surgery was not performed.
- Codes
- 0 Surgery of the primary site was performed.
- 1 Surgery of the primary site was not performed because it was not part of the planned first-course treatment.
- 2 Surgery of the primary site was not recommended/performed because it was contraindicated due to patient risk factors (comorbid conditions, advanced age, etc.).
- 5 Surgery of the primary site was not performed because the patient died prior to planned or recommended surgery.
- 6 Surgery of the primary site was not performed; it was recommended by the patient’s physician, but was not performed as part of the first-course therapy. No reason was noted in the patient’s record.
- 7 Surgery of the primary site was not performed; it was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record.
- 8 Surgery of the primary site was recommended, but it is unknown if it was performed. Further follow-up is recommended.
- 9 It is unknown if surgery of the primary site was recommended or performed. Death certificate-only cases and autopsy-only cases.
- Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1340
All data
st_css() #IMPORTANT!
# Put 10-19 into one catecory and 20-80 into one category
reasonfornosurgery <- as.factor(trimws(d[,"reasonfornosurgery"]))
levels(reasonfornosurgery)[levels(reasonfornosurgery)=="0"] <- "Site_performed.0"
levels(reasonfornosurgery)[levels(reasonfornosurgery)=="1"] <- "not_part_of_planned.1"
levels(reasonfornosurgery)[levels(reasonfornosurgery)=="2"] <- "Other_regional_sites.2"
levels(reasonfornosurgery)[levels(reasonfornosurgery)=="6"] <- "not_first_course_therapy.6"
levels(reasonfornosurgery)[levels(reasonfornosurgery)=="7"] <- "Recommended_refused.7"
levels(reasonfornosurgery)[levels(reasonfornosurgery)=="8"] <- "Recommended_unknown.8"
levels(reasonfornosurgery)[levels(reasonfornosurgery)=="9"] <- "Unknown.9"
new.d <- data.frame(new.d, reasonfornosurgery)
new.d <- apply_labels(new.d, reasonfornosurgery = "reason_for_no_surgery")
#summary(new.d$rxsummsurgprimsite)
temp.d <- data.frame (new.d.1, reasonfornosurgery)
summarytools::view(dfSummary(new.d$reasonfornosurgery, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
reasonfornosurgery
[labelled, factor] |
reason_for_no_surgery |
1. Site_performed.0
2. not_part_of_planned.1
3. Other_regional_sites.2
4. not_first_course_therapy.
5. Recommended_refused.7
6. Recommended_unknown.8
7. Unknown.9 |
| 1014 | ( | 46.8% | ) | | 1063 | ( | 49.1% | ) | | 30 | ( | 1.4% | ) | | 3 | ( | 0.1% | ) | | 32 | ( | 1.5% | ) | | 20 | ( | 0.9% | ) | | 5 | ( | 0.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
reason_for_no_surgery
[factor] |
1. Site_performed.0
2. not_part_of_planned.1
3. Other_regional_sites.2
4. not_first_course_therapy.
5. Recommended_refused.7
6. Recommended_unknown.8
7. Unknown.9 |
| 105 | ( | 55.6% | ) | | 71 | ( | 37.6% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.2% | ) | | 5 | ( | 2.6% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
reason_for_no_surgery
[factor] |
1. Site_performed.0
2. not_part_of_planned.1
3. Other_regional_sites.2
4. not_first_course_therapy.
5. Recommended_refused.7
6. Recommended_unknown.8
7. Unknown.9 |
| 48 | ( | 27.6% | ) | | 120 | ( | 69.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
reason_for_no_surgery
[factor] |
1. Site_performed.0
2. not_part_of_planned.1
3. Other_regional_sites.2
4. not_first_course_therapy.
5. Recommended_refused.7
6. Recommended_unknown.8
7. Unknown.9 |
| 144 | ( | 60.8% | ) | | 86 | ( | 36.3% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.7% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
reason_for_no_surgery
[factor] |
1. Site_performed.0
2. not_part_of_planned.1
3. Other_regional_sites.2
4. not_first_course_therapy.
5. Recommended_refused.7
6. Recommended_unknown.8
7. Unknown.9 |
| 85 | ( | 48.9% | ) | | 81 | ( | 46.6% | ) | | 5 | ( | 2.9% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
reason_for_no_surgery
[factor] |
1. Site_performed.0
2. not_part_of_planned.1
3. Other_regional_sites.2
4. not_first_course_therapy.
5. Recommended_refused.7
6. Recommended_unknown.8
7. Unknown.9 |
| 182 | ( | 62.8% | ) | | 89 | ( | 30.7% | ) | | 9 | ( | 3.1% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 3 | ( | 1.0% | ) | | 4 | ( | 1.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
reason_for_no_surgery
[factor] |
1. Site_performed.0
2. not_part_of_planned.1
3. Other_regional_sites.2
4. not_first_course_therapy.
5. Recommended_refused.7
6. Recommended_unknown.8
7. Unknown.9 |
| 443 | ( | 40.8% | ) | | 607 | ( | 55.8% | ) | | 11 | ( | 1.0% | ) | | 1 | ( | 0.1% | ) | | 14 | ( | 1.3% | ) | | 11 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
reason_for_no_surgery
[factor] |
1. Site_performed.0
2. not_part_of_planned.1
3. Other_regional_sites.2
4. not_first_course_therapy.
5. Recommended_refused.7
6. Recommended_unknown.8
7. Unknown.9 |
| 7 | ( | 43.8% | ) | | 9 | ( | 56.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–RADIATION
Description: Codes for the type of radiation therapy performed as part of the first course of treatment. Note: Radiation to brain and central nervous system for leukemia and lung cases is coded as radiation in this field.
Codes
- 0 None
- 1 Beam radiation
- 2 Radioactive implants
- 3 Radioisotopes
- 4 Combination of 1 with 2 or 3
- 5 Radiation, NOS-method or source not specified
- 6 Currently allowable for historic cases only; see note below
- 7 Patient or patient’s guardian refused*
- 8 Radiation recommended, unknown if administered*
- 9 Unknown if radiation administered
Note: CoC discontinued collection of this item in 2003 when FORDS was implemented. For CoC, codes 7 and 8 were used for tumors diagnosed before 1996, but should have been converted to 0 in this field and to the appropriate code in the new field Reason for No Radiation [1430]. SEER continues to use codes 7 and 8 for all years. See Chapter V, Unresolved Issues, for further discussion.
In the SEER program, a code 2 for other radiation was used between 1973 and 1987. When the radiation codes were expanded to add codes ‘2’ radioactive implants and ‘3’ radioisotopes, all cases with a code ‘2’ and diagnosed in 1973-1987 were converted to a code ‘6’ radiation other than beam radiation.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1360
All data
st_css() #IMPORTANT!
rxsummradiation <- as.factor(trimws(d[,"rxsummradiation"]))
levels(rxsummradiation) <- list(None.0="0",
Beam_radiation.1="1",
Radioactive_implants.2="2",
Radioisotopes.3="3",
Combination_12_or_13.4="4",
NOS_or_source_not_specified.5="5",
Historic_cases_only.6="6",
Refused.7="7",
Radiation_recommended_unknown.8="8",
Unknown.9 = "9")
new.d <- data.frame(new.d, rxsummradiation)
new.d <- apply_labels(new.d, rxsummradiation = "Type of radiation therapy")
#summary(new.d$rxsummradiation)
temp.d <- data.frame (new.d.1, rxsummradiation)
summarytools::view(dfSummary(new.d$rxsummradiation, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummradiation
[labelled, factor] |
Type of radiation therapy |
1. None.0
2. Beam_radiation.1
3. Radioactive_implants.2
4. Radioisotopes.3
5. Combination_12_or_13.4
6. NOS_or_source_not_specifi
7. Historic_cases_only.6
8. Refused.7
9. Radiation_recommended_unk
10. Unknown.9 |
| 1254 | ( | 58.5% | ) | | 546 | ( | 25.5% | ) | | 124 | ( | 5.8% | ) | | 3 | ( | 0.1% | ) | | 149 | ( | 7.0% | ) | | 3 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 0.6% | ) | | 41 | ( | 1.9% | ) | | 11 | ( | 0.5% | ) |
|
 |
24
(1.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_radiation
[factor] |
1. None.0
2. Beam_radiation.1
3. Radioactive_implants.2
4. Radioisotopes.3
5. Combination_12_or_13.4
6. NOS_or_source_not_specifi
7. Historic_cases_only.6
8. Refused.7
9. Radiation_recommended_unk
10. Unknown.9 |
| 129 | ( | 68.3% | ) | | 42 | ( | 22.2% | ) | | 4 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.6% | ) | | 8 | ( | 4.2% | ) | | 1 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_radiation
[factor] |
1. None.0
2. Beam_radiation.1
3. Radioactive_implants.2
4. Radioisotopes.3
5. Combination_12_or_13.4
6. NOS_or_source_not_specifi
7. Historic_cases_only.6
8. Refused.7
9. Radiation_recommended_unk
10. Unknown.9 |
| 96 | ( | 55.2% | ) | | 50 | ( | 28.7% | ) | | 16 | ( | 9.2% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 6.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_radiation
[factor] |
1. None.0
2. Beam_radiation.1
3. Radioactive_implants.2
4. Radioisotopes.3
5. Combination_12_or_13.4
6. NOS_or_source_not_specifi
7. Historic_cases_only.6
8. Refused.7
9. Radiation_recommended_unk
10. Unknown.9 |
| 165 | ( | 69.6% | ) | | 45 | ( | 19.0% | ) | | 10 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.5% | ) | | 5 | ( | 2.1% | ) | | 2 | ( | 0.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_radiation
[factor] |
1. None.0
2. Beam_radiation.1
3. Radioactive_implants.2
4. Radioisotopes.3
5. Combination_12_or_13.4
6. NOS_or_source_not_specifi
7. Historic_cases_only.6
8. Refused.7
9. Radiation_recommended_unk
10. Unknown.9 |
| 103 | ( | 67.8% | ) | | 36 | ( | 23.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.3% | ) | | 2 | ( | 1.3% | ) |
|
 |
22
(12.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_radiation
[factor] |
1. None.0
2. Beam_radiation.1
3. Radioactive_implants.2
4. Radioisotopes.3
5. Combination_12_or_13.4
6. NOS_or_source_not_specifi
7. Historic_cases_only.6
8. Refused.7
9. Radiation_recommended_unk
10. Unknown.9 |
| 185 | ( | 63.8% | ) | | 92 | ( | 31.7% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_radiation
[factor] |
1. None.0
2. Beam_radiation.1
3. Radioactive_implants.2
4. Radioisotopes.3
5. Combination_12_or_13.4
6. NOS_or_source_not_specifi
7. Historic_cases_only.6
8. Refused.7
9. Radiation_recommended_unk
10. Unknown.9 |
| 565 | ( | 52.1% | ) | | 277 | ( | 25.5% | ) | | 92 | ( | 8.5% | ) | | 3 | ( | 0.3% | ) | | 118 | ( | 10.9% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 26 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_radiation
[factor] |
1. None.0
2. Beam_radiation.1
3. Radioactive_implants.2
4. Radioisotopes.3
5. Combination_12_or_13.4
6. NOS_or_source_not_specifi
7. Historic_cases_only.6
8. Refused.7
9. Radiation_recommended_unk
10. Unknown.9 |
| 11 | ( | 68.8% | ) | | 4 | ( | 25.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–SURG/RAD SEQ
Description: Codes for the sequencing of radiation and surgery given as part of the first course of treatment. See also RX Summ–Surg Prim Site [1290], RX Summ–Scope LN Surg [1292], RX Summ–Surg Oth Reg/Dis [1294], and RX Summ–Radiation [1360].
Codes
- 0 No radiation and/or no surgery; unknown if surgery and/or radiation given
- 2 Radiation before surgery
- 3 Radiation after surgery
- 4 Radiation both before and after surgery
- 5 Intraoperative radiation
- 6 Intraoperative radiation with other radiation given before and/or after surgery
- 7 Surgery both before and after radiation
- 9 Sequence unknown, but both surgery and radiation were given
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1380
All data
st_css() #IMPORTANT!
rxsummsurgradseq <- as.factor(trimws(d[,"rxsummsurgradseq"]))
levels(rxsummsurgradseq) <- list(No_radiation_surgery.0="0",
Before_surgery.2="2",
After_surgery.3="3",
Both_before_after_surg.4="4",
Intraoperative_radiation.5="5",
Intraoperative_with_other.6="6",
Both_before_after_radia.7="7",
Unknown_Sequence.9 = "9")
new.d <- data.frame(new.d, rxsummsurgradseq)
new.d <- apply_labels(new.d, rxsummsurgradseq = "rx_summ_surg_rad_seq")
#summary(new.d$rxsummsurgradseq)
temp.d <- data.frame (new.d.1, rxsummsurgradseq)
summarytools::view(dfSummary(new.d$rxsummsurgradseq, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummsurgradseq
[labelled, factor] |
rx_summ_surg_rad_seq |
1. No_radiation_surgery.0
2. Before_surgery.2
3. After_surgery.3
4. Both_before_after_surg.4
5. Intraoperative_radiation.
6. Intraoperative_with_other
7. Both_before_after_radia.7
8. Unknown_Sequence.9 |
| 2059 | ( | 95.0% | ) | | 3 | ( | 0.1% | ) | | 101 | ( | 4.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_rad_seq
[factor] |
1. No_radiation_surgery.0
2. Before_surgery.2
3. After_surgery.3
4. Both_before_after_surg.4
5. Intraoperative_radiation.
6. Intraoperative_with_other
7. Both_before_after_radia.7
8. Unknown_Sequence.9 |
| 184 | ( | 97.4% | ) | | 1 | ( | 0.5% | ) | | 4 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_rad_seq
[factor] |
1. No_radiation_surgery.0
2. Before_surgery.2
3. After_surgery.3
4. Both_before_after_surg.4
5. Intraoperative_radiation.
6. Intraoperative_with_other
7. Both_before_after_radia.7
8. Unknown_Sequence.9 |
| 169 | ( | 97.1% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_rad_seq
[factor] |
1. No_radiation_surgery.0
2. Before_surgery.2
3. After_surgery.3
4. Both_before_after_surg.4
5. Intraoperative_radiation.
6. Intraoperative_with_other
7. Both_before_after_radia.7
8. Unknown_Sequence.9 |
| 229 | ( | 96.6% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_rad_seq
[factor] |
1. No_radiation_surgery.0
2. Before_surgery.2
3. After_surgery.3
4. Both_before_after_surg.4
5. Intraoperative_radiation.
6. Intraoperative_with_other
7. Both_before_after_radia.7
8. Unknown_Sequence.9 |
| 161 | ( | 92.5% | ) | | 0 | ( | 0.0% | ) | | 13 | ( | 7.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_rad_seq
[factor] |
1. No_radiation_surgery.0
2. Before_surgery.2
3. After_surgery.3
4. Both_before_after_surg.4
5. Intraoperative_radiation.
6. Intraoperative_with_other
7. Both_before_after_radia.7
8. Unknown_Sequence.9 |
| 267 | ( | 92.1% | ) | | 0 | ( | 0.0% | ) | | 22 | ( | 7.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_rad_seq
[factor] |
1. No_radiation_surgery.0
2. Before_surgery.2
3. After_surgery.3
4. Both_before_after_surg.4
5. Intraoperative_radiation.
6. Intraoperative_with_other
7. Both_before_after_radia.7
8. Unknown_Sequence.9 |
| 1034 | ( | 95.1% | ) | | 2 | ( | 0.2% | ) | | 49 | ( | 4.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_surg_rad_seq
[factor] |
1. No_radiation_surgery.0
2. Before_surgery.2
3. After_surgery.3
4. Both_before_after_surg.4
5. Intraoperative_radiation.
6. Intraoperative_with_other
7. Both_before_after_radia.7
8. Unknown_Sequence.9 |
| 15 | ( | 93.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–CHEMO
Description: Codes for chemotherapy given as part of the first course of treatment or the reason chemotherapy was not given. Includes treatment given at all facilities as part of the first course. Note: Prior to 2013, targeted therapies that invoke an immune response, such as Herceptin, had been coded as chemotherapy. Effective with cases diagnosed January 1, 2013, and forward these therapies are classified as biological response modifiers. Coding instructions for these changes have been added to the remarks field for the applicable drugs in the SEER*RX Interactive Drug Database ( http://seer.cancer.gov/tools/seerrx/).
Codes (Refer to the most recent version of STORE for additional instructions.)
- 00 None, chemotherapy was not part of the planned first course of therapy.
- 01 Chemotherapy, NOS.
- 02 Chemotherapy, single agent.
- 03 Chemotherapy, multiple agents.
- 82 Chemotherapy was not recommended/administered because it was contraindicated due to patient risk factors (i.e., comorbid conditions, advanced age).
- 85 Chemotherapy was not administered because the patient died prior to planned or recommended therapy.
- 86 Chemotherapy was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record.
- 87 Chemotherapy was not administered; it was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record.
- 88 Chemotherapy was recommended, but it is unknown if it was administered.
- 99 It is unknown whether a chemotherapeutic agent(s) was recommended or administered because it is not stated in patient record; death certificate-only cases.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1390
All data
st_css() #IMPORTANT!
rxsummchemo <- as.factor(trimws(d[,"rxsummchemo"]))
levels(rxsummchemo) <- list(None.0="0",
Chemo_NOS.1="1",
Chemo_single_agent.2="2",
Chemo_multiple_agents.3="3",
Not_recom_contraindicated.82="82",
Not_admin_first_course_trp.86="86",
Not_admin_refused.87="87",
Unknown_if_administered.88="88",
unknown.99="99")
new.d <- data.frame(new.d, rxsummchemo)
new.d <- apply_labels(new.d, rxsummchemo = "Chemotherapy as part of the first course of treatment")
#summary(new.d$rxsummchemo)
temp.d <- data.frame (new.d.1, rxsummchemo)
summarytools::view(dfSummary(new.d$rxsummchemo, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummchemo
[labelled, factor] |
Chemotherapy as part of the first
course of treatment |
1. None.0
2. Chemo_NOS.1
3. Chemo_single_agent.2
4. Chemo_multiple_agents.3
5. Not_recom_contraindicated
6. Not_admin_first_course_tr
7. Not_admin_refused.87
8. Unknown_if_administered.8
9. unknown.99 |
| 2121 | ( | 97.9% | ) | | 1 | ( | 0.0% | ) | | 24 | ( | 1.1% | ) | | 4 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 4 | ( | 0.2% | ) | | 12 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_chemo
[factor] |
1. None.0
2. Chemo_NOS.1
3. Chemo_single_agent.2
4. Chemo_multiple_agents.3
5. Not_recom_contraindicated
6. Not_admin_first_course_tr
7. Not_admin_refused.87
8. Unknown_if_administered.8
9. unknown.99 |
| 188 | ( | 99.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_chemo
[factor] |
1. None.0
2. Chemo_NOS.1
3. Chemo_single_agent.2
4. Chemo_multiple_agents.3
5. Not_recom_contraindicated
6. Not_admin_first_course_tr
7. Not_admin_refused.87
8. Unknown_if_administered.8
9. unknown.99 |
| 170 | ( | 97.7% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_chemo
[factor] |
1. None.0
2. Chemo_NOS.1
3. Chemo_single_agent.2
4. Chemo_multiple_agents.3
5. Not_recom_contraindicated
6. Not_admin_first_course_tr
7. Not_admin_refused.87
8. Unknown_if_administered.8
9. unknown.99 |
| 229 | ( | 96.6% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_chemo
[factor] |
1. None.0
2. Chemo_NOS.1
3. Chemo_single_agent.2
4. Chemo_multiple_agents.3
5. Not_recom_contraindicated
6. Not_admin_first_course_tr
7. Not_admin_refused.87
8. Unknown_if_administered.8
9. unknown.99 |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_chemo
[factor] |
1. None.0
2. Chemo_NOS.1
3. Chemo_single_agent.2
4. Chemo_multiple_agents.3
5. Not_recom_contraindicated
6. Not_admin_first_course_tr
7. Not_admin_refused.87
8. Unknown_if_administered.8
9. unknown.99 |
| 278 | ( | 95.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 3.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_chemo
[factor] |
1. None.0
2. Chemo_NOS.1
3. Chemo_single_agent.2
4. Chemo_multiple_agents.3
5. Not_recom_contraindicated
6. Not_admin_first_course_tr
7. Not_admin_refused.87
8. Unknown_if_administered.8
9. unknown.99 |
| 1066 | ( | 98.1% | ) | | 1 | ( | 0.1% | ) | | 11 | ( | 1.0% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_chemo
[factor] |
1. None.0
2. Chemo_NOS.1
3. Chemo_single_agent.2
4. Chemo_multiple_agents.3
5. Not_recom_contraindicated
6. Not_admin_first_course_tr
7. Not_admin_refused.87
8. Unknown_if_administered.8
9. unknown.99 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–HORMONE
Description: Records whether systemic hormonal agents were administered as first-course treatment at any facility, or the reason they were not given. Hormone therapy consists of a group of drugs that may affect the long-term control of a cancer’s growth. It is not usually used as a curative measure.
Rationale: Systemic therapy may involve the administration of one or a combination of agents. This data item allows for the evaluation of the administration of hormonal agents as part of the first course of therapy.
Codes (Refer to the most recent version of STORE and the SEER Program Code Manual for additional instructions.)
- 00 None, hormone therapy was not part of the planned first course of therapy.
- 01 Hormone therapy administered as first course therapy.
- 82 Hormone therapy was not recommended/administered because it was contraindicated due to patient risk factors (i.e., comorbid conditions, advanced age).
- 85 Hormone therapy was not administered because the patient died prior to planned or recommended therapy.
- 86 Hormone therapy was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record.
- 87 Hormone therapy was not administered. It was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record.
- 88 Hormone therapy was recommended, but it is unknown if it was administered.
- 99 It is unknown whether a hormonal agent(s) was recommended or administered because it is not stated in the patient record. Death certificate-only cases.
Note: For tumors diagnosed on or after January 1, 2003, information on endocrine surgery and/or endocrine radiation should be coded in the new field, RX Summ–Transplnt/Endocr [3250]. The CoC standards for hospitals do not allow use of codes 02-03 in tumors diagnosed on or after January 1, 2003.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1400
All data
st_css() #IMPORTANT!
rxsummhormone <- as.factor(trimws(d[,"rxsummhormone"]))
levels(rxsummhormone) <- list(None.0="0",
HT_first_course.1="1",
Not_recom_contraindicated.82="82",
Not_admin_died.85="85",
Recom_not_admin.86="86",
Recomm_but_refused.87="87",
Unknown_if_admin.88="88",
Unknown.99="99")
new.d <- data.frame(new.d, rxsummhormone)
new.d <- apply_labels(new.d, rxsummhormone = "Hormonal agents as part of the first course of treatment")
#summary(new.d$rxsummhormone)
temp.d <- data.frame (new.d.1, rxsummhormone)
summarytools::view(dfSummary(new.d$rxsummhormone, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummhormone
[labelled, factor] |
Hormonal agents as part of the first
course of treatment |
1. None.0
2. HT_first_course.1
3. Not_recom_contraindicated
4. Not_admin_died.85
5. Recom_not_admin.86
6. Recomm_but_refused.87
7. Unknown_if_admin.88
8. Unknown.99 |
| 1633 | ( | 75.4% | ) | | 471 | ( | 21.7% | ) | | 2 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) | | 21 | ( | 1.0% | ) | | 18 | ( | 0.8% | ) | | 17 | ( | 0.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_hormone
[factor] |
1. None.0
2. HT_first_course.1
3. Not_recom_contraindicated
4. Not_admin_died.85
5. Recom_not_admin.86
6. Recomm_but_refused.87
7. Unknown_if_admin.88
8. Unknown.99 |
| 148 | ( | 78.3% | ) | | 38 | ( | 20.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_hormone
[factor] |
1. None.0
2. HT_first_course.1
3. Not_recom_contraindicated
4. Not_admin_died.85
5. Recom_not_admin.86
6. Recomm_but_refused.87
7. Unknown_if_admin.88
8. Unknown.99 |
| 125 | ( | 71.8% | ) | | 46 | ( | 26.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_hormone
[factor] |
1. None.0
2. HT_first_course.1
3. Not_recom_contraindicated
4. Not_admin_died.85
5. Recom_not_admin.86
6. Recomm_but_refused.87
7. Unknown_if_admin.88
8. Unknown.99 |
| 188 | ( | 79.3% | ) | | 43 | ( | 18.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_hormone
[factor] |
1. None.0
2. HT_first_course.1
3. Not_recom_contraindicated
4. Not_admin_died.85
5. Recom_not_admin.86
6. Recomm_but_refused.87
7. Unknown_if_admin.88
8. Unknown.99 |
| 129 | ( | 74.1% | ) | | 43 | ( | 24.7% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_hormone
[factor] |
1. None.0
2. HT_first_course.1
3. Not_recom_contraindicated
4. Not_admin_died.85
5. Recom_not_admin.86
6. Recomm_but_refused.87
7. Unknown_if_admin.88
8. Unknown.99 |
| 200 | ( | 69.0% | ) | | 77 | ( | 26.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.0% | ) | | 10 | ( | 3.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_hormone
[factor] |
1. None.0
2. HT_first_course.1
3. Not_recom_contraindicated
4. Not_admin_died.85
5. Recom_not_admin.86
6. Recomm_but_refused.87
7. Unknown_if_admin.88
8. Unknown.99 |
| 828 | ( | 76.2% | ) | | 223 | ( | 20.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 0.4% | ) | | 14 | ( | 1.3% | ) | | 13 | ( | 1.2% | ) | | 5 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_hormone
[factor] |
1. None.0
2. HT_first_course.1
3. Not_recom_contraindicated
4. Not_admin_died.85
5. Recom_not_admin.86
6. Recomm_but_refused.87
7. Unknown_if_admin.88
8. Unknown.99 |
| 15 | ( | 93.8% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–BRM
Description:Records whether immunotherapeutic (biologic response modifiers) agents were administered as first-course treatment at all facilities or the reason they were not given. Immunotherapy consists of biological or chemical agents that alter the immune system or change the host’s response to tumor cells.
Rationale: Systemic therapy may involve the administration of one or a combination of agents. This data item allows for the evaluation of the administration of immunotherapeutic agents as part of the first course of therapy.
Note: Prior to 2013, targeted therapies that invoke an immune response, such as Herceptin, had been coded as chemotherapy. Effective with cases diagnosed January 1, 2013, and forward these therapies are classified as biological response modifiers. Coding instructions for these changes have been added to the remarks field for the applicable drugs in the SEER*RX Interactive Drug Database ( http://seer.cancer.gov/tools/seerrx/).
Codes (Refer to the most recent version of STORE and the SEER Program Code Manual for additional instructions.)
- 00 None, immunotherapy was not part of the planned first course of therapy.
- 01 Immunotherapy administered as first course therapy.
- 82 Immunotherapy was not recommended/administered because it was contraindicated due to patient risk factors (i.e., comorbid conditions, advanced age).
- 85 Immunotherapy was not administered because the patient died prior to planned or recommended therapy.
- 86 Immunotherapy was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record.
- 87 Immunotherapy was not administered. It was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian. The refusal was noted in the patient record.
- 88 Immunotherapy was recommended, but it is unknown if it was administered.
- 99 It is unknown whether an immunotherapeutic agent(s) was recommended or administered because it is not stated in patient record; death certificate-only cases.
Note: For tumors diagnosed on or after January 1, 2003, information on bone marrow transplants and stem cell transplants should be coded in the new field, RX SUMM–Transplnt/Endocr [3250]. The CoC standards for hospitals do not allow use of codes 02-06 in tumors diagnosed on or after January 1, 2003.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1410
All data
st_css() #IMPORTANT!
rxsummbrm <- as.factor(trimws(d[,"rxsummbrm"]))
levels(rxsummbrm) <- list(None.0="0",
Immunotherapy_first_course.1="1",
Recomm_not_admin.86="86",
Unknown_if_admin.88="88",
Unknown.99="99")
new.d <- data.frame(new.d, rxsummbrm)
new.d <- apply_labels(new.d, rxsummbrm = "rx_summ_brm")
#summary(new.d$rxsummbrm)
temp.d <- data.frame (new.d.1, rxsummbrm)
summarytools::view(dfSummary(new.d$rxsummbrm, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummbrm
[labelled, factor] |
rx_summ_brm |
1. None.0
2. Immunotherapy_first_cours
3. Recomm_not_admin.86
4. Unknown_if_admin.88
5. Unknown.99 |
| 2149 | ( | 99.2% | ) | | 5 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 11 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_brm
[factor] |
1. None.0
2. Immunotherapy_first_cours
3. Recomm_not_admin.86
4. Unknown_if_admin.88
5. Unknown.99 |
| 189 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_brm
[factor] |
1. None.0
2. Immunotherapy_first_cours
3. Recomm_not_admin.86
4. Unknown_if_admin.88
5. Unknown.99 |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_brm
[factor] |
1. None.0
2. Immunotherapy_first_cours
3. Recomm_not_admin.86
4. Unknown_if_admin.88
5. Unknown.99 |
| 236 | ( | 99.6% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_brm
[factor] |
1. None.0
2. Immunotherapy_first_cours
3. Recomm_not_admin.86
4. Unknown_if_admin.88
5. Unknown.99 |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_brm
[factor] |
1. None.0
2. Immunotherapy_first_cours
3. Recomm_not_admin.86
4. Unknown_if_admin.88
5. Unknown.99 |
| 279 | ( | 96.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 3.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_brm
[factor] |
1. None.0
2. Immunotherapy_first_cours
3. Recomm_not_admin.86
4. Unknown_if_admin.88
5. Unknown.99 |
| 1081 | ( | 99.4% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_brm
[factor] |
1. None.0
2. Immunotherapy_first_cours
3. Recomm_not_admin.86
4. Unknown_if_admin.88
5. Unknown.99 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–OTHER
Description:Identifies other treatment given at all facilities that cannot be defined as surgery, radiation, or systemic therapy according to the defined data items in this manual. Treatment for reportable hematopoietic diseases can be supportive care, observation, or any treatment that does not meet the usual definition in which treatment modifies, controls, removes, or destroys proliferating cancer tissue. Such treatments include phlebotomy, transfusions, and aspirin.
Rationale: Information on other therapy is used to describe and evaluate the quality-of-care and treatment practices.
Codes (Refer to the most recent version of STORE and the SEER Program Code Manual for additional instructions.)
- 0 None
- 1 Other
- 2 Other Experimental
- 3 Other-Double Blind
- 6 Other-Unproven
- 7 Refusal
- 8 Recommended
- 9 Unknown; unknown if administered
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1420
All data
st_css() #IMPORTANT!
rxsummother <- as.factor(trimws(d[,"rxsummother"]))
levels(rxsummother) <- list(None.0="0",
Other.1="1",
Other_Unproven.86="6",
Unknown.9="9")
new.d <- data.frame(new.d, rxsummother)
new.d <- apply_labels(new.d, rxsummother = "rx_summ_other")
#summary(new.d$rxsummother)
temp.d <- data.frame (new.d.1, rxsummother)
summarytools::view(dfSummary(new.d$rxsummother, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummother
[labelled, factor] |
rx_summ_other |
1. None.0
2. Other.1
3. Other_Unproven.86
4. Unknown.9 |
| 2153 | ( | 99.4% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 11 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_other
[factor] |
1. None.0
2. Other.1
3. Other_Unproven.86
4. Unknown.9 |
| 189 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_other
[factor] |
1. None.0
2. Other.1
3. Other_Unproven.86
4. Unknown.9 |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_other
[factor] |
1. None.0
2. Other.1
3. Other_Unproven.86
4. Unknown.9 |
| 236 | ( | 99.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_other
[factor] |
1. None.0
2. Other.1
3. Other_Unproven.86
4. Unknown.9 |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_other
[factor] |
1. None.0
2. Other.1
3. Other_Unproven.86
4. Unknown.9 |
| 279 | ( | 96.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 3.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_other
[factor] |
1. None.0
2. Other.1
3. Other_Unproven.86
4. Unknown.9 |
| 1085 | ( | 99.8% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_other
[factor] |
1. None.0
2. Other.1
3. Other_Unproven.86
4. Unknown.9 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RAD–REGIONAL DOSE: CGY
Description: The dominant or most clinically significant total dose of regional radiation therapy delivered to the patient during the first course of treatment. The unit of measure is centiGray (cGy). See also Rad–Regional RX Modality [1570].
Codes (in addition to actual doses)
- (Fill spaces) Record the actual regional dose delivered
- 00000 Radiation therapy was not administered
- 88888 Not applicable, brachytherapy or radioisotopes administered to the patient
- 99999 Regional radiation therapy was administered, but the dose is unknown
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1510
All data
st_css() #IMPORTANT!
radregionaldosecgy <- as.factor(trimws(d[,"radregionaldosecgy"]))
new.d <- data.frame(new.d, radregionaldosecgy)
new.d <- apply_labels(new.d, radregionaldosecgy = "rad_regional_dose_cgy")
temp.d <- data.frame (new.d.1, radregionaldosecgy)
summarytools::view(dfSummary(new.d$radregionaldosecgy, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
radregionaldosecgy
[labelled, factor] |
rad_regional_dose_cgy |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RAD–REGIONAL RX MODALITY
Description: Records the dominant modality of radiation therapy used to deliver the clinically most significant regional dose to the primary volume of interest during the first course of treatment.
Rationale: Radiation treatment frequently is delivered in two or more phases that can be summarized as regional and boost treatments. To evaluate patterns of radiation oncology care, it is necessary to know which radiation resources were employed in the delivery of therapy. For outcomes analysis, the modalities used for each of these phases can be very important.
Codes
- 00 No radiation treatment
- 20 External beam, NOS
- 21 Orthovoltage
- 22 Cobalt-60, Cesium-137
- 23 Photons (2-5 MV)
- 24 Photons (6-10 MV)
- 25 Photons (11-19 MV)
- 26 Photons (> 19 MV)
- 27 Photons (mixed energies)
- 28 Electrons
- 29 Photons and electrons mixed
- 30 Neutrons, with or without photons/electrons
- 31 IMRT
- 32 Conformal or 3-D therapy
- 40 Protons
- 41 Stereotactic radiosurgery, NOS
- 42 Linac radiosurgery
- 43 Gamma Knife
- 50 Brachytherapy, NOS
- 51 Brachytherapy, Intracavitary, Low Dose Rate (LDR)
- 52 Brachytherapy, Intracavitary, High Dose Rate (HDR)
- 53 Brachytherapy, Interstitial, Low Dose Rate (LDR)
- 54 Brachytherapy, Interstitial, High Dose Rate (HDR)
- 55 Radium
- 60 Radio-isotopes, NOS
- 61 Strontium - 89
- 62 Strontium - 90
- 80* Combination modality, specified
- 85* Combination modality, NOS
- 98 Other, NOS
- 99 Unknown
Note: For tumors diagnosed prior to January 1, 2003, the codes reported in this data item describe any radiation administered to the patient as part or all of the first course of therapy.
*Codes 80 and 85 describe specific converted descriptions of radiation therapy coded according to Volume II ROADS, and DAM rules and should only be used to record regional radiation for tumors diagnosed prior to January 1, 2003.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1570
All data
st_css() #IMPORTANT!
radregionalrxmodality <- as.factor(trimws(d[,"radregionalrxmodality"]))
levels(radregionalrxmodality) <- list(No_tx.0="0",
External_beam.20="20",
Photons_6_10_MV.24="24",
Photons_11_19_MV.25="25",
Photons_19more_MV.26="26",
Photons_mixed.27="27",
IMRT.31="31",
Conformal_or_3D_therapy.32="32",
Protons.40="40",
Stereotactic_radiosurgery.41 = "41",
Linac_radiosurgery.42 = "42",
Brachytherapy_NOS.50 = "50",
Brachytherapy_Intracavitary_LDR.51 = "51",
Brachytherapy_Intracavitary_HDR.52 = "52",
Brachytherapy_Interstitial_LDR.53 = "53",
Brachytherapy_Interstitial_HDR.54 = "54",
Radium.55 = "55",
Radio_isotopes.60 = "60",
Combination_modality_specified.80 = "80",
Other_NOS.98 = "98",
Unknown.99 = "99"
)
new.d <- data.frame(new.d, radregionalrxmodality)
new.d <- apply_labels(new.d, radregionalrxmodality = "rad_regional_rx_modality")
#summary(new.d$radregionalrxmodality)
temp.d <- data.frame (new.d.1, radregionalrxmodality)
summarytools::view(dfSummary(new.d$radregionalrxmodality, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
radregionalrxmodality
[labelled, factor] |
rad_regional_rx_modality |
1. No_tx.0
2. External_beam.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_19more_MV.26
6. Photons_mixed.27
7. IMRT.31
8. Conformal_or_3D_therapy.3
9. Protons.40
10. Stereotactic_radiosurgery
11. Linac_radiosurgery.42
12. Brachytherapy_NOS.50
13. Brachytherapy_Intracavita
14. Brachytherapy_Intracavita
15. Brachytherapy_Interstitia
16. Brachytherapy_Interstitia
17. Radium.55
18. Radio_isotopes.60
19. Combination_modality_spec
20. Other_NOS.98
21. Unknown.99 |
| 1191 | ( | 60.3% | ) | | 58 | ( | 2.9% | ) | | 130 | ( | 6.6% | ) | | 7 | ( | 0.4% | ) | | 2 | ( | 0.1% | ) | | 10 | ( | 0.5% | ) | | 374 | ( | 18.9% | ) | | 10 | ( | 0.5% | ) | | 7 | ( | 0.4% | ) | | 7 | ( | 0.4% | ) | | 17 | ( | 0.9% | ) | | 14 | ( | 0.7% | ) | | 3 | ( | 0.2% | ) | | 3 | ( | 0.2% | ) | | 84 | ( | 4.3% | ) | | 42 | ( | 2.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 9 | ( | 0.5% | ) |
|
 |
192
(8.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_regional_rx_modality
[factor] |
1. No_tx.0
2. External_beam.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_19more_MV.26
6. Photons_mixed.27
7. IMRT.31
8. Conformal_or_3D_therapy.3
9. Protons.40
10. Stereotactic_radiosurgery
11. Linac_radiosurgery.42
12. Brachytherapy_NOS.50
13. Brachytherapy_Intracavita
14. Brachytherapy_Intracavita
15. Brachytherapy_Interstitia
16. Brachytherapy_Interstitia
17. Radium.55
18. Radio_isotopes.60
19. Combination_modality_spec
20. Other_NOS.98
21. Unknown.99 |
| 140 | ( | 74.1% | ) | | 10 | ( | 5.3% | ) | | 5 | ( | 2.6% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 12.2% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 3 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_regional_rx_modality
[factor] |
1. No_tx.0
2. External_beam.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_19more_MV.26
6. Photons_mixed.27
7. IMRT.31
8. Conformal_or_3D_therapy.3
9. Protons.40
10. Stereotactic_radiosurgery
11. Linac_radiosurgery.42
12. Brachytherapy_NOS.50
13. Brachytherapy_Intracavita
14. Brachytherapy_Intracavita
15. Brachytherapy_Interstitia
16. Brachytherapy_Interstitia
17. Radium.55
18. Radio_isotopes.60
19. Combination_modality_spec
20. Other_NOS.98
21. Unknown.99 |
| 96 | ( | 55.2% | ) | | 7 | ( | 4.0% | ) | | 13 | ( | 7.5% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 33 | ( | 19.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 7 | ( | 4.0% | ) | | 9 | ( | 5.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_regional_rx_modality
[factor] |
1. No_tx.0
2. External_beam.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_19more_MV.26
6. Photons_mixed.27
7. IMRT.31
8. Conformal_or_3D_therapy.3
9. Protons.40
10. Stereotactic_radiosurgery
11. Linac_radiosurgery.42
12. Brachytherapy_NOS.50
13. Brachytherapy_Intracavita
14. Brachytherapy_Intracavita
15. Brachytherapy_Interstitia
16. Brachytherapy_Interstitia
17. Radium.55
18. Radio_isotopes.60
19. Combination_modality_spec
20. Other_NOS.98
21. Unknown.99 |
| 176 | ( | 74.3% | ) | | 6 | ( | 2.5% | ) | | 13 | ( | 5.5% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 24 | ( | 10.1% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.1% | ) | | 5 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_regional_rx_modality
[factor] |
1. No_tx.0
2. External_beam.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_19more_MV.26
6. Photons_mixed.27
7. IMRT.31
8. Conformal_or_3D_therapy.32
9. Protons.40
10. Stereotactic_radiosurgery.41
11. Linac_radiosurgery.42
12. Brachytherapy_NOS.50
13. Brachytherapy_Intracavitary_LDR.51
14. Brachytherapy_Intracavitary_HDR.52
15. Brachytherapy_Interstitial_LDR.53
16. Brachytherapy_Interstitial_HDR.54
17. Radium.55
18. Radio_isotopes.60
19. Combination_modality_specified.80
20. Other_NOS.98
21. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_regional_rx_modality
[factor] |
1. No_tx.0
2. External_beam.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_19more_MV.26
6. Photons_mixed.27
7. IMRT.31
8. Conformal_or_3D_therapy.3
9. Protons.40
10. Stereotactic_radiosurgery
11. Linac_radiosurgery.42
12. Brachytherapy_NOS.50
13. Brachytherapy_Intracavita
14. Brachytherapy_Intracavita
15. Brachytherapy_Interstitia
16. Brachytherapy_Interstitia
17. Radium.55
18. Radio_isotopes.60
19. Combination_modality_spec
20. Other_NOS.98
21. Unknown.99 |
| 186 | ( | 64.1% | ) | | 4 | ( | 1.4% | ) | | 27 | ( | 9.3% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 59 | ( | 20.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_regional_rx_modality
[factor] |
1. No_tx.0
2. External_beam.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_19more_MV.26
6. Photons_mixed.27
7. IMRT.31
8. Conformal_or_3D_therapy.3
9. Protons.40
10. Stereotactic_radiosurgery
11. Linac_radiosurgery.42
12. Brachytherapy_NOS.50
13. Brachytherapy_Intracavita
14. Brachytherapy_Intracavita
15. Brachytherapy_Interstitia
16. Brachytherapy_Interstitia
17. Radium.55
18. Radio_isotopes.60
19. Combination_modality_spec
20. Other_NOS.98
21. Unknown.99 |
| 593 | ( | 54.7% | ) | | 31 | ( | 2.9% | ) | | 72 | ( | 6.6% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 0.6% | ) | | 235 | ( | 21.7% | ) | | 6 | ( | 0.6% | ) | | 3 | ( | 0.3% | ) | | 4 | ( | 0.4% | ) | | 15 | ( | 1.4% | ) | | 10 | ( | 0.9% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 72 | ( | 6.6% | ) | | 27 | ( | 2.5% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_regional_rx_modality
[factor] |
1. No_tx.0
2. External_beam.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_19more_MV.26
6. Photons_mixed.27
7. IMRT.31
8. Conformal_or_3D_therapy.32
9. Protons.40
10. Stereotactic_radiosurgery.41
11. Linac_radiosurgery.42
12. Brachytherapy_NOS.50
13. Brachytherapy_Intracavitary_LDR.51
14. Brachytherapy_Intracavitary_HDR.52
15. Brachytherapy_Interstitial_LDR.53
16. Brachytherapy_Interstitial_HDR.54
17. Radium.55
18. Radio_isotopes.60
19. Combination_modality_specified.80
20. Other_NOS.98
21. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–SYSTEMIC/SUR SEQ
Description: Records the sequencing of systemic therapy (RX Summ-Chemo [1390], RX Summ-Hormone [1400], RX Summ-BRM [1410], and RX Summ-Transplnt/Endocr [3250]) and surgical procedures given as part of the first course of treatment. See also RX Summ–Surg Prim Site [1290], RX Summ–Scope LN Surg [1292], and RX Summ–Surg Oth Reg/Dis [1294].
Rationale: The sequence of systemic therapy and surgical procedures given as part of the first course of treatment cannot always be determined using the date on which each modality was started or performed. This data item can be used to more precisely evaluate the time of delivery of treatment to the patient.
Codes
- 0 No systemic therapy and/or surgical procedures; unknown if surgery and/or systemic therapy given
- 2 Systemic therapy before surgery
- 3 Systemic therapy after surgery
- 4 Systemic therapy both before and after surgery
- 5 Intraoperative systemic therapy
- 6 Intraoperative systemic therapy with other therapy administered before and/or after surgery
- 7 Surgery both before and after systemic therapy
- 9 Sequence unknown, but both surgery and systemic therapy given
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1639
All data
st_css() #IMPORTANT!
rxsummsystemicsurseq <- as.factor(trimws(d[,"rxsummsystemicsurseq"]))
levels(rxsummsystemicsurseq) <- list(No.0="0",
Therapy_before_surg.2="2",
Therapy_after_surg.3="3",
Both_before_after.4="4",
Sequence_unknown_not_given.9="9"
)
new.d <- data.frame(new.d, rxsummsystemicsurseq)
new.d <- apply_labels(new.d, rxsummsystemicsurseq = "rx_summ_systemic_sur_seq")
#summary(new.d$rxsummsystemicsurseq)
temp.d <- data.frame (new.d.1, rxsummsystemicsurseq)
summarytools::view(dfSummary(new.d$rxsummsystemicsurseq, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummsystemicsurseq
[labelled, factor] |
rx_summ_systemic_sur_seq |
1. No.0
2. Therapy_before_surg.2
3. Therapy_after_surg.3
4. Both_before_after.4
5. Sequence_unknown_not_give |
| 2085 | ( | 96.2% | ) | | 15 | ( | 0.7% | ) | | 63 | ( | 2.9% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_systemic_sur_seq
[factor] |
1. No.0
2. Therapy_before_surg.2
3. Therapy_after_surg.3
4. Both_before_after.4
5. Sequence_unknown_not_give |
| 178 | ( | 94.2% | ) | | 4 | ( | 2.1% | ) | | 6 | ( | 3.2% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_systemic_sur_seq
[factor] |
1. No.0
2. Therapy_before_surg.2
3. Therapy_after_surg.3
4. Both_before_after.4
5. Sequence_unknown_not_give |
| 169 | ( | 97.1% | ) | | 2 | ( | 1.1% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_systemic_sur_seq
[factor] |
1. No.0
2. Therapy_before_surg.2
3. Therapy_after_surg.3
4. Both_before_after.4
5. Sequence_unknown_not_give |
| 228 | ( | 96.2% | ) | | 4 | ( | 1.7% | ) | | 4 | ( | 1.7% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_systemic_sur_seq
[factor] |
1. No.0
2. Therapy_before_surg.2
3. Therapy_after_surg.3
4. Both_before_after.4
5. Sequence_unknown_not_give |
| 165 | ( | 94.8% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 5.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_systemic_sur_seq
[factor] |
1. No.0
2. Therapy_before_surg.2
3. Therapy_after_surg.3
4. Both_before_after.4
5. Sequence_unknown_not_give |
| 278 | ( | 95.9% | ) | | 2 | ( | 0.7% | ) | | 9 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_systemic_sur_seq
[factor] |
1. No.0
2. Therapy_before_surg.2
3. Therapy_after_surg.3
4. Both_before_after.4
5. Sequence_unknown_not_give |
| 1051 | ( | 96.7% | ) | | 3 | ( | 0.3% | ) | | 32 | ( | 2.9% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_summ_systemic_sur_seq
[factor] |
1. No.0
2. Therapy_before_surg.2
3. Therapy_after_surg.3
4. Both_before_after.4
5. Sequence_unknown_not_give |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–SURG SITE 98-02
- Description: Site-specific codes for the type of surgery to the primary site performed as part of the first course of treatment. This includes treatment given at all facilities as part of the first course of treatment. This field is to be used for ROADS codes after the ROADS to FORDS conversion. It is also to be used to code Surgery Primary Site at all facilities for all tumors diagnosed before January 1, 2003.
- Rationale: If central registries wish to study the treatment given at particular hospitals, the hospital-level treatment fields must be used.
- Codes (in addition to the site-specific codes)
- 00 No primary site surgery performed
- 99 Unknown if primary site surgery performed
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1646
All data
st_css() #IMPORTANT!
rxsummsurgsite9802 <- as.factor(trimws(d[,"rxsummsurgsite9802"]))
new.d <- data.frame(new.d, rxsummsurgsite9802)
new.d <- apply_labels(new.d, rxsummsurgsite9802 = "rx_summ_surg_site_9802")
temp.d <- data.frame (new.d.1, rxsummsurgsite9802)
summarytools::view(dfSummary(new.d$rxsummsurgsite9802, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummsurgsite9802
[labelled, factor] |
rx_summ_surg_site_9802 |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–SCOPE REG 98-02
- Description: Describes the removal, biopsy or aspiration of regional lymph node(s) at the time of surgery of the primary site or during a separate surgical event at all facilities. This field is to be used for ROADS codes after the ROADS to FORDS conversion. It is also to be used to code Scope of Regional Lymph Node Surgery at all facilities for all tumors diagnosed before January 1, 2003.
- Rationale: In evaluating quality of care and treatment practices it is important to identify the removal, biopsy, or aspiration of regional lymph node(s) at the time of surgery of the primary site or during a separate surgical event.
- Codes (See the CoC ROADS Manual 1998 Supplement and the SEER Program Code Manual for site-specific codes.)
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1647
All data
st_css() #IMPORTANT!
rxsummscopereg9802 <- as.factor(trimws(d[,"rxsummscopereg9802"]))
new.d <- data.frame(new.d, rxsummscopereg9802)
new.d <- apply_labels(new.d, rxsummscopereg9802 = "rx_summ_scope_reg_9802")
temp.d <- data.frame (new.d.1, rxsummscopereg9802)
summarytools::view(dfSummary(new.d$rxsummscopereg9802, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummscopereg9802
[labelled, factor] |
rx_summ_scope_reg_9802 |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–SCOPE OTH 98-02
- Description: Records the surgical removal of distant lymph nodes or other tissue(s)/organ(s) beyond the primary site given at all facilities as part of the first course of treatment. This field is to be used for ROADS codes after the ROADS to FORDS conversion. It is also to be used to code Surgery Regional/Distant Sites at all facilities for all tumors diagnosed before January 1, 2003.
- Rationale: The removal of non-primary tissue documents the extent of surgical treatment and is useful in evaluating the extent of metastatic involvement.
- Codes (See the CoC ROADS Manual 1998 Supplement and the SEER Program Code Manual for site-specific codes.)
- Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1648
All data
st_css() #IMPORTANT!
rxsummsurgoth9802 <- as.factor(trimws(d[,"rxsummsurgoth9802"]))
new.d <- data.frame(new.d, rxsummsurgoth9802)
new.d <- apply_labels(new.d, rxsummsurgoth9802 = "rx_summ_surg_oth_9802")
temp.d <- data.frame (new.d.1, rxsummsurgoth9802)
summarytools::view(dfSummary(new.d$rxsummsurgoth9802, style = 'grid', max.distinct.values = 100, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummsurgoth9802
[labelled, factor] |
rx_summ_surg_oth_9802 |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
VITAL STATUS
All data
st_css() #IMPORTANT!
vitalstatus <- as.factor(trimws(d[,"vitalstatus"]))
levels(vitalstatus) <- list(Dead.0="0",
Alive.1="1")
new.d <- data.frame(new.d, vitalstatus)
new.d <- apply_labels(new.d, vitalstatus = "Vital status of the patient")
#summary(new.d$vitalstatus)
temp.d <- data.frame (new.d.1, vitalstatus)
summarytools::view(dfSummary(new.d$vitalstatus, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
vitalstatus
[labelled, factor] |
Vital status of the patient |
1. Dead.0
2. Alive.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
vital_status
[factor] |
1. Dead.0
2. Alive.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
vital_status
[factor] |
1. Dead.0
2. Alive.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
vital_status
[factor] |
1. Dead.0
2. Alive.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
vital_status
[factor] |
1. Dead.0
2. Alive.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
vital_status
[factor] |
1. Dead.0
2. Alive.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
vital_status
[factor] |
1. Dead.0
2. Alive.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
vital_status
[factor] |
1. Dead.0
2. Alive.1 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SURV-DATE ACTIVE FOLLOWUP
- Description: The Surv-Date Active Followup is defined as the earlier of the Date of Last Contact [1750] and a study cutoff date. The study cut-off date is a pre-determined date based on the year of data submission and is set in the survival program used to derive the seven survival variables. If the Date of Last Contact [1750] is earlier than the study cut-off date and either the day or month is unknown or not available, the values are imputed by the survival program. The survival program is available from your standard setter or NAACCR.
Example 1 Date of Last Contact: 20111120 Study Cut-off Date: 20111231 Surv-Date Active Followup: 20111120 Note: The date of last contact is earlier than the study cut-off date, and the date of last contact is complete, so the date of last contact is used in Surv-Date Active Followup.
Example 2 Date of Last Contact: 201111 Study Cut-off Date: 20111231 Surv-Date Active Followup: 20111115 Note: Rationale is to take mid-point of possible values. For Nov (30 days) it would be FLOOR((1+30)/2) = 15, where FLOOR is a function that rounds a decimal down to an integer.
Rationale: The Surv-Date Active Followup is needed to be able to recalculate survival months if a different study cut-off date is used and provides flexibility to recalculate survival without needing to rerun the survival program on the original data. Additional information about the survival algorithm and what specific values are assigned in given missing date situations are available here: http://seer.cancer.gov/survivaltime/.
Codes: If Date of Last Contact [1750] is blank, Surv-Date Active Followup will also be blank.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1782
All data
st_css() #IMPORTANT!
survdateactivefollowup <- as.factor(d[,"survdateactivefollowup"])
new.d <- data.frame(new.d, survdateactivefollowup)
new.d <- apply_labels(new.d, survdateactivefollowup = "survdate_active_follow_up")
#summary(new.d$survdateactivefollowup)
temp.d <- data.frame (new.d.1, survdateactivefollowup)
summarytools::view(dfSummary(new.d$survdateactivefollowup, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdateactivefollowup
[labelled, factor] |
survdate_active_follow_up |
1. 20150709
2. 20150722
3. 20150730
4. 20150810
5. 20150824
6. 20150915
7. 20151110
8. 20151113
9. 20151120
10. 20151216
11. 20160109
12. 20160322
13. 20160324
14. 20160402
15. 20160404
16. 20160519
17. 20160599
18. 20160708
19. 20160805
20. 20160812
21. 20161009
22. 20161031
23. 201611 ·
24. 20161101
25. 20161103
26. 20161114
27. 20161121
28. 20161124
29. 20161199
30. 20161206
31. 20161208
32. 20161221
33. 20170103
34. 20170110
35. 20170111
36. 20170118
37. 20170124
38. 20170223
39. 201703 ·
40. 20170306
41. 20170309
42. 20170330
43. 20170401
44. 20170404
45. 20170524
46. 20170609
47. 20170628
48. 20170711
49. 20170712
50. 20170713
[ 52 others ] |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1875 | ( | 97.2% | ) |
|
 |
237
(10.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_active_follow_up
[factor]
EAN-8 codes |
1. 20150709
2. 20150722
3. 20150730
4. 20150810
5. 20150824
6. 20150915
7. 20151110
8. 20151113
9. 20151120
10. 20151216
11. 20160109
12. 20160322
13. 20160324
14. 20160402
15. 20160404
16. 20160519
17. 20160599
18. 20160708
19. 20160805
20. 20160812
21. 20161009
22. 20161031
23. 201611 ·
24. 20161101
25. 20161103
26. 20161114
27. 20161121
28. 20161124
29. 20161199
30. 20161206
31. 20161208
32. 20161221
33. 20170103
34. 20170110
35. 20170111
36. 20170118
37. 20170124
38. 20170223
39. 201703 ·
40. 20170306
41. 20170309
42. 20170330
43. 20170401
44. 20170404
45. 20170524
46. 20170609
47. 20170628
48. 20170711
49. 20170712
50. 20170713
[ 52 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 183 | ( | 96.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_active_follow_up
[factor] |
1. 20150709
2. 20150722
3. 20150730
4. 20150810
5. 20150824
6. 20150915
7. 20151110
8. 20151113
9. 20151120
10. 20151216
11. 20160109
12. 20160322
13. 20160324
14. 20160402
15. 20160404
16. 20160519
17. 20160599
18. 20160708
19. 20160805
20. 20160812
21. 20161009
22. 20161031
23. 201611 ·
24. 20161101
25. 20161103
26. 20161114
27. 20161121
28. 20161124
29. 20161199
30. 20161206
31. 20161208
32. 20161221
33. 20170103
34. 20170110
35. 20170111
36. 20170118
37. 20170124
38. 20170223
39. 201703 ·
40. 20170306
41. 20170309
42. 20170330
43. 20170401
44. 20170404
45. 20170524
46. 20170609
47. 20170628
48. 20170711
49. 20170712
50. 20170713
[ 52 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 173 | ( | 99.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_active_follow_up
[factor] |
1. 20150709
2. 20150722
3. 20150730
4. 20150810
5. 20150824
6. 20150915
7. 20151110
8. 20151113
9. 20151120
10. 20151216
11. 20160109
12. 20160322
13. 20160324
14. 20160402
15. 20160404
16. 20160519
17. 20160599
18. 20160708
19. 20160805
20. 20160812
21. 20161009
22. 20161031
23. 201611 ·
24. 20161101
25. 20161103
26. 20161114
27. 20161121
28. 20161124
29. 20161199
30. 20161206
31. 20161208
32. 20161221
33. 20170103
34. 20170110
35. 20170111
36. 20170118
37. 20170124
38. 20170223
39. 201703 ·
40. 20170306
41. 20170309
42. 20170330
43. 20170401
44. 20170404
45. 20170524
46. 20170609
47. 20170628
48. 20170711
49. 20170712
50. 20170713
51. 20170724
52. 20170727
53. 201708 ·
54. 20170811
55. 20170821
56. 20170830
57. 20171025
58. 20171206
59. 201801 ·
60. 20180109
61. 20180118
62. 20180126
63. 20180201
64. 20180212
65. 20180219
66. 20180306
67. 20180307
68. 201804 ·
69. 20180507
70. 20180509
71. 201806 ·
72. 20180619
73. 20180625
74. 20180803
75. 20180810
76. 20180817
77. 20180820
78. 20180906
79. 20180908
80. 20181001
81. 20181005
82. 20181016
83. 20181025
84. 201811 ·
85. 20181106
86. 20181107
87. 20181130
88. 20181199
89. 20181204
90. 20181214
91. 20181219
92. 20181231
93. 20181299
94. 201901 ·
95. 201902 ·
96. 201904 ·
97. 201905 ·
98. 201906 ·
99. 201907 ·
100. 201909 ·
101. 201911 ·
102. 201912 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_active_follow_up
[factor] |
1. 20150709
2. 20150722
3. 20150730
4. 20150810
5. 20150824
6. 20150915
7. 20151110
8. 20151113
9. 20151120
10. 20151216
11. 20160109
12. 20160322
13. 20160324
14. 20160402
15. 20160404
16. 20160519
17. 20160599
18. 20160708
19. 20160805
20. 20160812
21. 20161009
22. 20161031
23. 201611 ·
24. 20161101
25. 20161103
26. 20161114
27. 20161121
28. 20161124
29. 20161199
30. 20161206
31. 20161208
32. 20161221
33. 20170103
34. 20170110
35. 20170111
36. 20170118
37. 20170124
38. 20170223
39. 201703 ·
40. 20170306
41. 20170309
42. 20170330
43. 20170401
44. 20170404
45. 20170524
46. 20170609
47. 20170628
48. 20170711
49. 20170712
50. 20170713
[ 52 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 172 | ( | 98.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_active_follow_up
[factor] |
1. 20150709
2. 20150722
3. 20150730
4. 20150810
5. 20150824
6. 20150915
7. 20151110
8. 20151113
9. 20151120
10. 20151216
11. 20160109
12. 20160322
13. 20160324
14. 20160402
15. 20160404
16. 20160519
17. 20160599
18. 20160708
19. 20160805
20. 20160812
21. 20161009
22. 20161031
23. 201611 ·
24. 20161101
25. 20161103
26. 20161114
27. 20161121
28. 20161124
29. 20161199
30. 20161206
31. 20161208
32. 20161221
33. 20170103
34. 20170110
35. 20170111
36. 20170118
37. 20170124
38. 20170223
39. 201703 ·
40. 20170306
41. 20170309
42. 20170330
43. 20170401
44. 20170404
45. 20170524
46. 20170609
47. 20170628
48. 20170711
49. 20170712
50. 20170713
[ 52 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 281 | ( | 96.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_active_follow_up
[factor] |
1. 20150709
2. 20150722
3. 20150730
4. 20150810
5. 20150824
6. 20150915
7. 20151110
8. 20151113
9. 20151120
10. 20151216
11. 20160109
12. 20160322
13. 20160324
14. 20160402
15. 20160404
16. 20160519
17. 20160599
18. 20160708
19. 20160805
20. 20160812
21. 20161009
22. 20161031
23. 201611 ·
24. 20161101
25. 20161103
26. 20161114
27. 20161121
28. 20161124
29. 20161199
30. 20161206
31. 20161208
32. 20161221
33. 20170103
34. 20170110
35. 20170111
36. 20170118
37. 20170124
38. 20170223
39. 201703 ·
40. 20170306
41. 20170309
42. 20170330
43. 20170401
44. 20170404
45. 20170524
46. 20170609
47. 20170628
48. 20170711
49. 20170712
50. 20170713
[ 52 others ] |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1050 | ( | 96.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_active_follow_up
[factor] |
1. 20150709
2. 20150722
3. 20150730
4. 20150810
5. 20150824
6. 20150915
7. 20151110
8. 20151113
9. 20151120
10. 20151216
11. 20160109
12. 20160322
13. 20160324
14. 20160402
15. 20160404
16. 20160519
17. 20160599
18. 20160708
19. 20160805
20. 20160812
21. 20161009
22. 20161031
23. 201611 ·
24. 20161101
25. 20161103
26. 20161114
27. 20161121
28. 20161124
29. 20161199
30. 20161206
31. 20161208
32. 20161221
33. 20170103
34. 20170110
35. 20170111
36. 20170118
37. 20170124
38. 20170223
39. 201703 ·
40. 20170306
41. 20170309
42. 20170330
43. 20170401
44. 20170404
45. 20170524
46. 20170609
47. 20170628
48. 20170711
49. 20170712
50. 20170713
[ 52 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 16 | ( | 100.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SURV-DATE PRESUMED ALIVE
- Description: The Surv-Date Presumed Alive is the last date for which complete death ascertainment is available from the registry at the time a file is transmitted. Because not all central cancer registries conduct active patient follow-up, it is necessary to have an option for calculating survival times based on the assumption that the registry has ascertained all available deaths (state/province and national), and persons not known to be deceased are presumed to be alive as of the last date for which complete death ascertainment is available. This variable is set in the survival program used to derive the seven survival variables. The survival program is available from your standard setter or NAACCR.
Example 1 Vital Status: Alive Date of Last Contact: 20111120 Study Cut-off Date: 20111231 Latest date for complete death ascertainment: 20111231 Surv-Date Presumed Alive: 20111231
Rationale: The Surv-Date Presumed Alive is needed to be able to recalculate survival months if a different study cut-off date is used and provides flexibility to recalculate survival without needing to rerun the survival program on the original data.
Additional information about the survival algorithm and what specific values are assigned in given missing date situations are available here: http://seer.cancer.gov/survivaltime/.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1785
All data
st_css() #IMPORTANT!
survdatepresumedalive <- as.factor(d[,"survdatepresumedalive"])
new.d <- data.frame(new.d, survdatepresumedalive)
new.d <- apply_labels(new.d, survdatepresumedalive = "survdate_presumed_alive")
#summary(new.d$survdatepresumedalive)
temp.d <- data.frame (new.d.1, survdatepresumedalive)
summarytools::view(dfSummary(new.d$survdatepresumedalive, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdatepresumedalive
[labelled, factor] |
survdate_presumed_alive |
1. 20181231
2. 20181299
3. 201912 · |
| 1551 | ( | 80.4% | ) | | 190 | ( | 9.8% | ) | | 189 | ( | 9.8% | ) |
|
 |
237
(10.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_presumed_alive
[factor]
EAN-8 codes |
1. 20181231
2. 20181299
3. 201912 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 189 | ( | 100.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_presumed_alive
[factor] |
1. 20181231
2. 20181299
3. 201912 · |
| 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_presumed_alive
[factor] |
1. 20181231
2. 20181299
3. 201912 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_presumed_alive
[factor] |
1. 20181231
2. 20181299
3. 201912 · |
| 0 | ( | 0.0% | ) | | 174 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_presumed_alive
[factor] |
1. 20181231
2. 20181299
3. 201912 · |
| 290 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_presumed_alive
[factor] |
1. 20181231
2. 20181299
3. 201912 · |
| 1087 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_presumed_alive
[factor] |
1. 20181231
2. 20181299
3. 201912 · |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SURV-DATE RX RECODE
- Description: The survival date of diagnosis recode is calculated using the month, day, and year of the Date of Diagnosis [390]. If the Date of Diagnosis [390] has complete month and day information, the Surv-Date Dx Recode will be the same as the Date of Diagnosis [390]. If the day or month is unknown or not available, the values are imputed by the survival program used to derive the seven survival variables. The survival program is available from your standard setter or NAACCR.
Example 1 Date of diagnosis: 20111199 Date of Last Contact: 20111120 Surv-Date of DX Recode: 20111110 Note: The recoded value is the mid-point between 11/1 and 11/20.
Example 2 Date of diagnosis: 2011 Date of Last Contact: 20111120 Surv-Date of DX Recode: 20110611 Note: The recoded value is the mid-point between 20110101 and 20111120.
Rationale: The Surv-Date DX Recode is needed to be able to match to a lifetable entry to obtain expected survival. If a case is diagnosed in January 2000, the first 12 months of expected survival will be from the 2000 life table. If a case is diagnosed in December 2000, only one month will be from the 2000 life table and then the 2001 life table is used.
Additional information about the survival algorithm and what specific values are assigned in given missing date situations are available here: http://seer.cancer.gov/survivaltime/.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#1788
All data
st_css() #IMPORTANT!
survdatedxrecode <- as.factor(d[,"survdatedxrecode"])
new.d <- data.frame(new.d, survdatedxrecode)
new.d <- apply_labels(new.d, survdatedxrecode = "survdate_dx_recode")
#summary(new.d$survdatedxrecode)
temp.d <- data.frame (new.d.1, survdatedxrecode)
summarytools::view(dfSummary(new.d$survdatedxrecode, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdatedxrecode
[labelled, factor] |
survdate_dx_recode |
1. 20110899
2. 20120399
3. 20120499
4. 20120699
5. 20130199
6. 20130399
7. 20130499
8. 20130699
9. 20131099
10. 20140299
11. 20140399
12. 20140699
13. 20140799
14. 20141299
15. 201501 ·
16. 20150101
17. 20150105
18. 20150106
19. 20150107
20. 20150108
21. 20150109
22. 20150112
23. 20150113
24. 20150114
25. 20150115
26. 20150116
27. 20150119
28. 20150120
29. 20150121
30. 20150122
31. 20150123
32. 20150126
33. 20150127
34. 20150128
35. 20150129
36. 20150130
37. 20150199
38. 201502 ·
39. 20150201
40. 20150202
41. 20150203
42. 20150204
43. 20150205
44. 20150206
45. 20150209
46. 20150210
47. 20150211
48. 20150212
49. 20150213
50. 20150215
[ 535 others ] |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 8 | ( | 0.4% | ) | | 3 | ( | 0.2% | ) | | 5 | ( | 0.3% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.3% | ) | | 4 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 3 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.2% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 3 | ( | 0.2% | ) | | 6 | ( | 0.3% | ) | | 4 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1816 | ( | 94.1% | ) |
|
 |
237
(10.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_dx_recode
[factor]
EAN-8 codes |
1. 20110899
2. 20120399
3. 20120499
4. 20120699
5. 20130199
6. 20130399
7. 20130499
8. 20130699
9. 20131099
10. 20140299
11. 20140399
12. 20140699
13. 20140799
14. 20141299
15. 201501 ·
16. 20150101
17. 20150105
18. 20150106
19. 20150107
20. 20150108
21. 20150109
22. 20150112
23. 20150113
24. 20150114
25. 20150115
26. 20150116
27. 20150119
28. 20150120
29. 20150121
30. 20150122
31. 20150123
32. 20150126
33. 20150127
34. 20150128
35. 20150129
36. 20150130
37. 20150199
38. 201502 ·
39. 20150201
40. 20150202
41. 20150203
42. 20150204
43. 20150205
44. 20150206
45. 20150209
46. 20150210
47. 20150211
48. 20150212
49. 20150213
50. 20150215
[ 535 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 177 | ( | 93.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_dx_recode
[factor] |
1. 20110899
2. 20120399
3. 20120499
4. 20120699
5. 20130199
6. 20130399
7. 20130499
8. 20130699
9. 20131099
10. 20140299
11. 20140399
12. 20140699
13. 20140799
14. 20141299
15. 201501 ·
16. 20150101
17. 20150105
18. 20150106
19. 20150107
20. 20150108
21. 20150109
22. 20150112
23. 20150113
24. 20150114
25. 20150115
26. 20150116
27. 20150119
28. 20150120
29. 20150121
30. 20150122
31. 20150123
32. 20150126
33. 20150127
34. 20150128
35. 20150129
36. 20150130
37. 20150199
38. 201502 ·
39. 20150201
40. 20150202
41. 20150203
42. 20150204
43. 20150205
44. 20150206
45. 20150209
46. 20150210
47. 20150211
48. 20150212
49. 20150213
50. 20150215
[ 535 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 162 | ( | 93.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_dx_recode
[factor] |
1. 20110899
2. 20120399
3. 20120499
4. 20120699
5. 20130199
6. 20130399
7. 20130499
8. 20130699
9. 20131099
10. 20140299
11. 20140399
12. 20140699
13. 20140799
14. 20141299
15. 201501 ·
16. 20150101
17. 20150105
18. 20150106
19. 20150107
20. 20150108
21. 20150109
22. 20150112
23. 20150113
24. 20150114
25. 20150115
26. 20150116
27. 20150119
28. 20150120
29. 20150121
30. 20150122
31. 20150123
32. 20150126
33. 20150127
34. 20150128
35. 20150129
36. 20150130
37. 20150199
38. 201502 ·
39. 20150201
40. 20150202
41. 20150203
42. 20150204
43. 20150205
44. 20150206
45. 20150209
46. 20150210
47. 20150211
48. 20150212
49. 20150213
50. 20150215
51. 20150216
52. 20150217
53. 20150218
54. 20150220
55. 20150223
56. 20150224
57. 20150227
58. 20150299
59. 201503 ·
60. 20150301
61. 20150302
62. 20150303
63. 20150304
64. 20150305
65. 20150306
66. 20150309
67. 20150310
68. 20150311
69. 20150312
70. 20150313
71. 20150315
72. 20150316
73. 20150317
74. 20150318
75. 20150319
76. 20150320
77. 20150323
78. 20150325
79. 20150326
80. 20150327
81. 20150330
82. 20150331
83. 20150399
84. 201504 ·
85. 20150401
86. 20150403
87. 20150406
88. 20150407
89. 20150408
90. 20150410
91. 20150411
92. 20150413
93. 20150414
94. 20150415
95. 20150416
96. 20150417
97. 20150420
98. 20150421
99. 20150422
100. 20150423
101. 20150424
102. 20150427
103. 20150428
104. 20150429
105. 20150430
106. 20150499
107. 201505 ·
108. 20150501
109. 20150502
110. 20150504
111. 20150505
112. 20150506
113. 20150507
114. 20150511
115. 20150512
116. 20150513
117. 20150514
118. 20150515
119. 20150519
120. 20150520
121. 20150521
122. 20150526
123. 20150527
124. 20150528
125. 20150529
126. 20150599
127. 201506 ·
128. 20150601
129. 20150602
130. 20150603
131. 20150604
132. 20150605
133. 20150608
134. 20150609
135. 20150610
136. 20150611
137. 20150612
138. 20150615
139. 20150616
140. 20150617
141. 20150618
142. 20150619
143. 20150622
144. 20150623
145. 20150624
146. 20150625
147. 20150626
148. 20150629
149. 20150630
150. 20150699
151. 201507 ·
152. 20150701
153. 20150702
154. 20150706
155. 20150707
156. 20150709
157. 20150710
158. 20150713
159. 20150714
160. 20150715
161. 20150716
162. 20150717
163. 20150720
164. 20150721
165. 20150722
166. 20150723
167. 20150724
168. 20150727
169. 20150728
170. 20150729
171. 20150730
172. 20150731
173. 20150799
174. 201508 ·
175. 20150801
176. 20150803
177. 20150804
178. 20150805
179. 20150806
180. 20150807
181. 20150810
182. 20150811
183. 20150812
184. 20150813
185. 20150814
186. 20150815
187. 20150818
188. 20150819
189. 20150820
190. 20150821
191. 20150824
192. 20150825
193. 20150826
194. 20150827
195. 20150828
196. 20150831
197. 20150899
198. 201509 ·
199. 20150901
200. 20150902
201. 20150908
202. 20150909
203. 20150910
204. 20150911
205. 20150912
206. 20150914
207. 20150915
208. 20150916
209. 20150917
210. 20150918
211. 20150921
212. 20150922
213. 20150924
214. 20150925
215. 20150928
216. 20150930
217. 20150999
218. 201510 ·
219. 20151001
220. 20151005
221. 20151007
222. 20151008
223. 20151012
224. 20151013
225. 20151014
226. 20151015
227. 20151016
228. 20151019
229. 20151020
230. 20151021
231. 20151022
232. 20151023
233. 20151026
234. 20151027
235. 20151028
236. 20151029
237. 20151030
238. 20151099
239. 201511 ·
240. 20151101
241. 20151102
242. 20151103
243. 20151104
244. 20151105
245. 20151106
246. 20151109
247. 20151110
248. 20151111
249. 20151112
250. 20151113
251. 20151116
252. 20151117
253. 20151119
254. 20151120
255. 20151123
256. 20151124
257. 20151127
258. 20151130
259. 20151199
260. 201512 ·
261. 20151201
262. 20151202
263. 20151203
264. 20151204
265. 20151207
266. 20151208
267. 20151209
268. 20151210
269. 20151211
270. 20151214
271. 20151215
272. 20151216
273. 20151217
274. 20151218
275. 20151221
276. 20151223
277. 20151228
278. 20151229
279. 20151230
280. 20151299
281. 201601 ·
282. 20160104
283. 20160105
284. 20160106
285. 20160107
286. 20160108
287. 20160111
288. 20160112
289. 20160113
290. 20160114
291. 20160115
292. 20160119
293. 20160120
294. 20160121
295. 20160122
296. 20160125
297. 20160126
298. 20160128
299. 20160129
300. 20160199
301. 201602 ·
302. 20160201
303. 20160202
304. 20160203
305. 20160204
306. 20160205
307. 20160208
308. 20160209
309. 20160210
310. 20160211
311. 20160212
312. 20160215
313. 20160216
314. 20160217
315. 20160218
316. 20160219
317. 20160223
318. 20160224
319. 20160225
320. 20160226
321. 20160229
322. 20160299
323. 201603 ·
324. 20160301
325. 20160302
326. 20160303
327. 20160304
328. 20160307
329. 20160308
330. 20160310
331. 20160311
332. 20160314
333. 20160315
334. 20160316
335. 20160317
336. 20160318
337. 20160321
338. 20160322
339. 20160323
340. 20160324
341. 20160325
342. 20160328
343. 20160329
344. 20160330
345. 20160331
346. 20160399
347. 201604 ·
348. 20160401
349. 20160404
350. 20160405
351. 20160406
352. 20160407
353. 20160408
354. 20160411
355. 20160412
356. 20160413
357. 20160414
358. 20160415
359. 20160418
360. 20160419
361. 20160420
362. 20160421
363. 20160422
364. 20160425
365. 20160426
366. 20160427
367. 20160428
368. 20160429
369. 20160499
370. 201605 ·
371. 20160502
372. 20160503
373. 20160504
374. 20160505
375. 20160506
376. 20160509
377. 20160510
378. 20160511
379. 20160512
380. 20160513
381. 20160516
382. 20160518
383. 20160519
384. 20160520
385. 20160523
386. 20160524
387. 20160525
388. 20160526
389. 20160527
390. 20160531
391. 20160599
392. 201606 ·
393. 20160601
394. 20160602
395. 20160603
396. 20160606
397. 20160607
398. 20160608
399. 20160609
400. 20160610
401. 20160613
402. 20160614
403. 20160615
404. 20160616
405. 20160617
406. 20160620
407. 20160621
408. 20160622
409. 20160623
410. 20160624
411. 20160626
412. 20160628
413. 20160630
414. 20160699
415. 201607 ·
416. 20160701
417. 20160705
418. 20160706
419. 20160707
420. 20160708
421. 20160711
422. 20160712
423. 20160713
424. 20160714
425. 20160715
426. 20160716
427. 20160718
428. 20160719
429. 20160720
430. 20160721
431. 20160722
432. 20160725
433. 20160726
434. 20160727
435. 20160728
436. 20160729
437. 20160799
438. 201608 ·
439. 20160801
440. 20160802
441. 20160803
442. 20160804
443. 20160805
444. 20160808
445. 20160809
446. 20160810
447. 20160811
448. 20160812
449. 20160815
450. 20160816
451. 20160817
452. 20160818
453. 20160819
454. 20160822
455. 20160823
456. 20160824
457. 20160825
458. 20160826
459. 20160829
460. 20160830
461. 20160831
462. 20160899
463. 201609 ·
464. 20160901
465. 20160902
466. 20160906
467. 20160907
468. 20160908
469. 20160909
470. 20160912
471. 20160913
472. 20160914
473. 20160915
474. 20160916
475. 20160919
476. 20160920
477. 20160921
478. 20160922
479. 20160923
480. 20160926
481. 20160928
482. 20160929
483. 20160930
484. 20160999
485. 201610 ·
486. 20161001
487. 20161003
488. 20161004
489. 20161005
490. 20161006
491. 20161007
492. 20161010
493. 20161011
494. 20161012
495. 20161013
496. 20161014
497. 20161017
498. 20161018
499. 20161019
500. 20161020
501. 20161021
502. 20161024
503. 20161026
504. 20161027
505. 20161028
506. 20161031
507. 20161099
508. 201611 ·
509. 20161101
510. 20161102
511. 20161103
512. 20161104
513. 20161107
514. 20161108
515. 20161109
516. 20161110
517. 20161114
518. 20161115
519. 20161116
520. 20161117
521. 20161118
522. 20161121
523. 20161122
524. 20161123
525. 20161128
526. 20161129
527. 20161130
528. 20161199
529. 201612 ·
530. 20161201
531. 20161202
532. 20161205
533. 20161206
534. 20161207
535. 20161208
536. 20161209
537. 20161210
538. 20161212
539. 20161213
540. 20161214
541. 20161216
542. 20161219
543. 20161220
544. 20161221
545. 20161222
546. 20161223
547. 20161227
548. 20161228
549. 20161230
550. 20161299
551. 20170106
552. 20170199
553. 201702 ·
554. 20170299
555. 201703 ·
556. 20170399
557. 20170413
558. 20170499
559. 20170599
560. 201706 ·
561. 20170699
562. 20170799
563. 201708 ·
564. 20170899
565. 20170911
566. 20170999
567. 20171099
568. 201711 ·
569. 20171199
570. 201712 ·
571. 20171299
572. 20180199
573. 20180227
574. 20180299
575. 20180399
576. 20180499
577. 20180599
578. 20180699
579. 20180799
580. 20180899
581. 20180926
582. 20180999
583. 20181099
584. 20181199
585. 20181299 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_dx_recode
[factor] |
1. 20110899
2. 20120399
3. 20120499
4. 20120699
5. 20130199
6. 20130399
7. 20130499
8. 20130699
9. 20131099
10. 20140299
11. 20140399
12. 20140699
13. 20140799
14. 20141299
15. 201501 ·
16. 20150101
17. 20150105
18. 20150106
19. 20150107
20. 20150108
21. 20150109
22. 20150112
23. 20150113
24. 20150114
25. 20150115
26. 20150116
27. 20150119
28. 20150120
29. 20150121
30. 20150122
31. 20150123
32. 20150126
33. 20150127
34. 20150128
35. 20150129
36. 20150130
37. 20150199
38. 201502 ·
39. 20150201
40. 20150202
41. 20150203
42. 20150204
43. 20150205
44. 20150206
45. 20150209
46. 20150210
47. 20150211
48. 20150212
49. 20150213
50. 20150215
[ 535 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 168 | ( | 96.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_dx_recode
[factor] |
1. 20110899
2. 20120399
3. 20120499
4. 20120699
5. 20130199
6. 20130399
7. 20130499
8. 20130699
9. 20131099
10. 20140299
11. 20140399
12. 20140699
13. 20140799
14. 20141299
15. 201501 ·
16. 20150101
17. 20150105
18. 20150106
19. 20150107
20. 20150108
21. 20150109
22. 20150112
23. 20150113
24. 20150114
25. 20150115
26. 20150116
27. 20150119
28. 20150120
29. 20150121
30. 20150122
31. 20150123
32. 20150126
33. 20150127
34. 20150128
35. 20150129
36. 20150130
37. 20150199
38. 201502 ·
39. 20150201
40. 20150202
41. 20150203
42. 20150204
43. 20150205
44. 20150206
45. 20150209
46. 20150210
47. 20150211
48. 20150212
49. 20150213
50. 20150215
[ 535 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 276 | ( | 95.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_dx_recode
[factor] |
1. 20110899
2. 20120399
3. 20120499
4. 20120699
5. 20130199
6. 20130399
7. 20130499
8. 20130699
9. 20131099
10. 20140299
11. 20140399
12. 20140699
13. 20140799
14. 20141299
15. 201501 ·
16. 20150101
17. 20150105
18. 20150106
19. 20150107
20. 20150108
21. 20150109
22. 20150112
23. 20150113
24. 20150114
25. 20150115
26. 20150116
27. 20150119
28. 20150120
29. 20150121
30. 20150122
31. 20150123
32. 20150126
33. 20150127
34. 20150128
35. 20150129
36. 20150130
37. 20150199
38. 201502 ·
39. 20150201
40. 20150202
41. 20150203
42. 20150204
43. 20150205
44. 20150206
45. 20150209
46. 20150210
47. 20150211
48. 20150212
49. 20150213
50. 20150215
[ 535 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1032 | ( | 94.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
survdate_dx_recode
[factor] |
1. 20110899
2. 20120399
3. 20120499
4. 20120699
5. 20130199
6. 20130399
7. 20130499
8. 20130699
9. 20131099
10. 20140299
11. 20140399
12. 20140699
13. 20140799
14. 20141299
15. 201501 ·
16. 20150101
17. 20150105
18. 20150106
19. 20150107
20. 20150108
21. 20150109
22. 20150112
23. 20150113
24. 20150114
25. 20150115
26. 20150116
27. 20150119
28. 20150120
29. 20150121
30. 20150122
31. 20150123
32. 20150126
33. 20150127
34. 20150128
35. 20150129
36. 20150130
37. 20150199
38. 201502 ·
39. 20150201
40. 20150202
41. 20150203
42. 20150204
43. 20150205
44. 20150206
45. 20150209
46. 20150210
47. 20150211
48. 20150212
49. 20150213
50. 20150215
[ 535 others ] |
| 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 2 | ( | 12.5% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
# CAUSE OF DEATH {.tabset} - Description: Official cause of death as coded from the death certificate in valid ICD-7, ICD-8, ICD-9, and ICD-10 codes. - Rationale: Cause of death is used for calculation of adjusted survival rates by the life table method. The adjustment corrects for deaths other than from the diagnosed cancer. - Note: This data item is no longer supported by CoC (as of January 1, 2003). - Special codes in addition to ICD-7, ICD-8, ICD-9, and ICD-10 (refer to SEER Program Code Manual for additional instructions.) + 0000 Patient alive at last contact + 7777 State death certificate not available + 7797 State death certificate available but underlying cause of death is not coded
All data
st_css() #IMPORTANT!
causeofdeath <- as.factor(trimws(d[,"causeofdeath"]))
levels(causeofdeath) <- list(Alive_last_contact.0="0",
Certificate_not_available.7777="7777"
)
new.d <- data.frame(new.d, causeofdeath)
new.d <- apply_labels(new.d, causeofdeath = "cause_of_death")
#summary(new.d$causeofdeath)
temp.d <- data.frame (new.d.1, causeofdeath)
summarytools::view(dfSummary(new.d$causeofdeath, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
causeofdeath
[labelled, factor] |
cause_of_death |
1. Alive_last_contact.0
2. Certificate_not_available |
|
 |
8
(0.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cause_of_death
[factor] |
1. Alive_last_contact.0
2. Certificate_not_available |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cause_of_death
[factor] |
1. Alive_last_contact.0
2. Certificate_not_available |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cause_of_death
[factor] |
1. Alive_last_contact.0
2. Certificate_not_available |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cause_of_death
[factor] |
1. Alive_last_contact.0
2. Certificate_not_available |
|
 |
3
(1.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cause_of_death
[factor] |
1. Alive_last_contact.0
2. Certificate_not_available |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cause_of_death
[factor] |
1. Alive_last_contact.0
2. Certificate_not_available |
|
 |
5
(0.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cause_of_death
[factor] |
1. Alive_last_contact.0
2. Certificate_not_available |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS EXTENSION
Description: Identifies contiguous growth (extension) of the primary tumor within the organ of origin or its direct extension into neighboring organs. For certain sites such as ovary, discontinuous metastasis is coded in CS Extension.
Rationale: Tumor extension at diagnosis is a prognostic indicator used by Collaborative Staging to derive some TNM-T codes and some SEER Summary Stage codes.
Codes (See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Note: For cases diagnosed prior to 2010, this was a 2 character field in CS version 1 which was converted to a 3 character field in CS version 2. Most 2 character codes were converted by adding a zero as the third character. For example, code 05 was usually converted to 050, 10 to 100, 11 to 110, etc. Special codes such as 88 and 99 were usually converted to 888 and 999, respectively.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2810
All data
st_css() #IMPORTANT!
csextension <- as.factor(trimws(d[,"csextension"]))
new.d <- data.frame(new.d, csextension)
new.d <- apply_labels(new.d, csextension = "cs_extension")
temp.d <- data.frame (new.d.1, csextension)
summarytools::view(dfSummary(new.d$csextension, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
csextension
[labelled, factor] |
cs_extension |
1. 100
2. 130
3. 140
4. 150
5. 200
6. 210
7. 220
8. 230
9. 240
10. 300
11. 410
12. 420
13. 430
14. 440
15. 445
16. 450
17. 490
18. 500
19. 600
20. 999 |
| 3 | ( | 0.2% | ) | | 6 | ( | 0.4% | ) | | 7 | ( | 0.4% | ) | | 1052 | ( | 66.2% | ) | | 26 | ( | 1.6% | ) | | 52 | ( | 3.3% | ) | | 32 | ( | 2.0% | ) | | 57 | ( | 3.6% | ) | | 23 | ( | 1.4% | ) | | 245 | ( | 15.4% | ) | | 9 | ( | 0.6% | ) | | 5 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 9 | ( | 0.6% | ) | | 23 | ( | 1.4% | ) | | 5 | ( | 0.3% | ) | | 6 | ( | 0.4% | ) | | 2 | ( | 0.1% | ) | | 24 | ( | 1.5% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_extension
[factor] |
1. 100
2. 130
3. 140
4. 150
5. 200
6. 210
7. 220
8. 230
9. 240
10. 300
11. 410
12. 420
13. 430
14. 440
15. 445
16. 450
17. 490
18. 500
19. 600
20. 999 |
| 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 64 | ( | 71.1% | ) | | 3 | ( | 3.3% | ) | | 5 | ( | 5.6% | ) | | 2 | ( | 2.2% | ) | | 3 | ( | 3.3% | ) | | 1 | ( | 1.1% | ) | | 5 | ( | 5.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.2% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_extension
[factor] |
1. 100
2. 130
3. 140
4. 150
5. 200
6. 210
7. 220
8. 230
9. 240
10. 300
11. 410
12. 420
13. 430
14. 440
15. 445
16. 450
17. 490
18. 500
19. 600
20. 999 |
| 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 57 | ( | 67.1% | ) | | 1 | ( | 1.2% | ) | | 9 | ( | 10.6% | ) | | 6 | ( | 7.1% | ) | | 1 | ( | 1.2% | ) | | 2 | ( | 2.4% | ) | | 5 | ( | 5.9% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_extension
[factor] |
1. 100
2. 130
3. 140
4. 150
5. 200
6. 210
7. 220
8. 230
9. 240
10. 300
11. 410
12. 420
13. 430
14. 440
15. 445
16. 450
17. 490
18. 500
19. 600
20. 999 |
| 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 1 | ( | 0.7% | ) | | 97 | ( | 69.8% | ) | | 4 | ( | 2.9% | ) | | 9 | ( | 6.5% | ) | | 4 | ( | 2.9% | ) | | 4 | ( | 2.9% | ) | | 3 | ( | 2.2% | ) | | 8 | ( | 5.8% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.9% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_extension
[factor] |
1. 100
2. 130
3. 140
4. 150
5. 200
6. 210
7. 220
8. 230
9. 240
10. 300
11. 410
12. 420
13. 430
14. 440
15. 445
16. 450
17. 490
18. 500
19. 600
20. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 33 | ( | 55.9% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 3 | ( | 5.1% | ) | | 4 | ( | 6.8% | ) | | 2 | ( | 3.4% | ) | | 12 | ( | 20.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_extension
[factor] |
1. 100
2. 130
3. 140
4. 150
5. 200
6. 210
7. 220
8. 230
9. 240
10. 300
11. 410
12. 420
13. 430
14. 440
15. 445
16. 450
17. 490
18. 500
19. 600
20. 999 |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 82 | ( | 71.3% | ) | | 5 | ( | 4.3% | ) | | 2 | ( | 1.7% | ) | | 3 | ( | 2.6% | ) | | 1 | ( | 0.9% | ) | | 7 | ( | 6.1% | ) | | 9 | ( | 7.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_extension
[factor] |
1. 100
2. 130
3. 140
4. 150
5. 200
6. 210
7. 220
8. 230
9. 240
10. 300
11. 410
12. 420
13. 430
14. 440
15. 445
16. 450
17. 490
18. 500
19. 600
20. 999 |
| 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 709 | ( | 65.3% | ) | | 12 | ( | 1.1% | ) | | 26 | ( | 2.4% | ) | | 14 | ( | 1.3% | ) | | 44 | ( | 4.1% | ) | | 8 | ( | 0.7% | ) | | 200 | ( | 18.4% | ) | | 7 | ( | 0.6% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 17 | ( | 1.6% | ) | | 3 | ( | 0.3% | ) | | 4 | ( | 0.4% | ) | | 1 | ( | 0.1% | ) | | 21 | ( | 1.9% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_extension
[factor] |
1. 100
2. 130
3. 140
4. 150
5. 200
6. 210
7. 220
8. 230
9. 240
10. 300
11. 410
12. 420
13. 430
14. 440
15. 445
16. 450
17. 490
18. 500
19. 600
20. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 62.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 37.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS LYMPH NODES
Description: Identifies the regional lymph nodes involved with cancer at the time of diagnosis.
Rationale: The involvement of specific regional lymph nodes is a prognostic indicator used by Collaborative Staging to derive some TNM-N codes and SEER Summary Stage codes.
Codes (See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Note: For cases prior to 2010, this was a 2 character field in CS version 1 which was converted to a 3 character field in CS version 2. Most 2 character codes were converted by adding a zero as the third character. For example, code 05 was usually converted to 050, 10 to 100, 11 to 110, etc. Special codes such as 88 and 99 were usually converted to 888 and 999 respectively.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2830
All data
st_css() #IMPORTANT!
cslymphnodes <- as.factor(trimws(d[,"cslymphnodes"]))
new.d <- data.frame(new.d, cslymphnodes)
new.d <- apply_labels(new.d, cslymphnodes = "cs_lymph_nodes")
temp.d <- data.frame (new.d.1, cslymphnodes)
summarytools::view(dfSummary(new.d$cslymphnodes, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cslymphnodes
[labelled, factor] |
cs_lymph_nodes |
1. 0
2. 100
3. 800
4. 999 |
| 1496 | ( | 94.1% | ) | | 64 | ( | 4.0% | ) | | 1 | ( | 0.1% | ) | | 28 | ( | 1.8% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_lymph_nodes
[factor] |
1. 0
2. 100
3. 800
4. 999 |
| 83 | ( | 92.2% | ) | | 7 | ( | 7.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_lymph_nodes
[factor] |
1. 0
2. 100
3. 800
4. 999 |
| 83 | ( | 97.6% | ) | | 2 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_lymph_nodes
[factor] |
1. 0
2. 100
3. 800
4. 999 |
| 125 | ( | 89.9% | ) | | 11 | ( | 7.9% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.2% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_lymph_nodes
[factor] |
1. 0
2. 100
3. 800
4. 999 |
| 57 | ( | 96.6% | ) | | 2 | ( | 3.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_lymph_nodes
[factor] |
1. 0
2. 100
3. 800
4. 999 |
| 109 | ( | 94.8% | ) | | 5 | ( | 4.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.9% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_lymph_nodes
[factor] |
1. 0
2. 100
3. 800
4. 999 |
| 1023 | ( | 94.3% | ) | | 37 | ( | 3.4% | ) | | 1 | ( | 0.1% | ) | | 24 | ( | 2.2% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_lymph_nodes
[factor] |
1. 0
2. 100
3. 800
4. 999 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS METS AT DX
Description: Identifies the distant site(s) of metastatic involvement at time of diagnosis.
Rationale: The presence of metastatic disease at diagnosis is an independent prognostic indicator, and it is used by Collaborative Staging to derive TNM-M codes and SEER Summary Stage codes.
Codes (See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2850
All data
st_css() #IMPORTANT!
csmetsatdx <- as.factor(trimws(d[,"csmetsatdx"]))
new.d <- data.frame(new.d, csmetsatdx)
new.d <- apply_labels(new.d, csmetsatdx = "cs_mets_at_dx")
temp.d <- data.frame (new.d.1, csmetsatdx)
summarytools::view(dfSummary(new.d$csmetsatdx, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
csmetsatdx
[labelled, factor] |
cs_mets_at_dx |
1. 0
2. 11
3. 12
4. 30
5. 35
6. 38
7. 40
8. 60
9. 99 |
| 1531 | ( | 96.3% | ) | | 1 | ( | 0.1% | ) | | 6 | ( | 0.4% | ) | | 24 | ( | 1.5% | ) | | 4 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 19 | ( | 1.2% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_mets_at_dx
[factor] |
1. 0
2. 11
3. 12
4. 30
5. 35
6. 38
7. 40
8. 60
9. 99 |
| 88 | ( | 97.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_mets_at_dx
[factor] |
1. 0
2. 11
3. 12
4. 30
5. 35
6. 38
7. 40
8. 60
9. 99 |
| 83 | ( | 97.6% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_mets_at_dx
[factor] |
1. 0
2. 11
3. 12
4. 30
5. 35
6. 38
7. 40
8. 60
9. 99 |
| 134 | ( | 96.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_mets_at_dx
[factor] |
1. 0
2. 11
3. 12
4. 30
5. 35
6. 38
7. 40
8. 60
9. 99 |
| 56 | ( | 94.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 5.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_mets_at_dx
[factor] |
1. 0
2. 11
3. 12
4. 30
5. 35
6. 38
7. 40
8. 60
9. 99 |
| 113 | ( | 98.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_mets_at_dx
[factor] |
1. 0
2. 11
3. 12
4. 30
5. 35
6. 38
7. 40
8. 60
9. 99 |
| 1041 | ( | 95.9% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 0.5% | ) | | 15 | ( | 1.4% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 17 | ( | 1.6% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_mets_at_dx
[factor] |
1. 0
2. 11
3. 12
4. 30
5. 35
6. 38
7. 40
8. 60
9. 99 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 7
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 7 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2861
All data
st_css() #IMPORTANT!
cssitespecificfactor7 <- as.factor(trimws(d[,"cssitespecificfactor7"]))
new.d <- data.frame(new.d, cssitespecificfactor7)
new.d <- apply_labels(new.d, cssitespecificfactor7 = "cs_site_specific_factor7")
temp.d <- data.frame (new.d.1, cssitespecificfactor7)
summarytools::view(dfSummary(new.d$cssitespecificfactor7, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor7
[labelled, factor] |
cs_site_specific_factor7 |
1. 23
2. 32
3. 33
4. 34
5. 35
6. 43
7. 44
8. 45
9. 53
10. 54
11. 55
12. 99
13. 998
14. 999 |
| 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 479 | ( | 22.5% | ) | | 609 | ( | 28.6% | ) | | 33 | ( | 1.5% | ) | | 450 | ( | 21.1% | ) | | 290 | ( | 13.6% | ) | | 141 | ( | 6.6% | ) | | 6 | ( | 0.3% | ) | | 49 | ( | 2.3% | ) | | 19 | ( | 0.9% | ) | | 23 | ( | 1.1% | ) | | 5 | ( | 0.2% | ) | | 25 | ( | 1.2% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor7
[factor] |
1. 23
2. 32
3. 33
4. 34
5. 35
6. 43
7. 44
8. 45
9. 53
10. 54
11. 55
12. 99
13. 998
14. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 57 | ( | 30.2% | ) | | 54 | ( | 28.6% | ) | | 3 | ( | 1.6% | ) | | 35 | ( | 18.5% | ) | | 12 | ( | 6.3% | ) | | 13 | ( | 6.9% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.2% | ) | | 1 | ( | 0.5% | ) | | 4 | ( | 2.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor7
[factor] |
1. 23
2. 32
3. 33
4. 34
5. 35
6. 43
7. 44
8. 45
9. 53
10. 54
11. 55
12. 99
13. 998
14. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 62 | ( | 35.6% | ) | | 59 | ( | 33.9% | ) | | 2 | ( | 1.1% | ) | | 29 | ( | 16.7% | ) | | 9 | ( | 5.2% | ) | | 7 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor7
[factor] |
1. 23
2. 32
3. 33
4. 34
5. 35
6. 43
7. 44
8. 45
9. 53
10. 54
11. 55
12. 99
13. 998
14. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 64 | ( | 27.0% | ) | | 69 | ( | 29.1% | ) | | 2 | ( | 0.8% | ) | | 34 | ( | 14.3% | ) | | 35 | ( | 14.8% | ) | | 17 | ( | 7.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) | | 3 | ( | 1.3% | ) | | 6 | ( | 2.5% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor7
[factor] |
1. 23
2. 32
3. 33
4. 34
5. 35
6. 43
7. 44
8. 45
9. 53
10. 54
11. 55
12. 99
13. 998
14. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 16.2% | ) | | 39 | ( | 27.5% | ) | | 0 | ( | 0.0% | ) | | 39 | ( | 27.5% | ) | | 28 | ( | 19.7% | ) | | 7 | ( | 4.9% | ) | | 1 | ( | 0.7% | ) | | 3 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor7
[factor] |
1. 23
2. 32
3. 33
4. 34
5. 35
6. 43
7. 44
8. 45
9. 53
10. 54
11. 55
12. 99
13. 998
14. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 45 | ( | 15.5% | ) | | 76 | ( | 26.2% | ) | | 12 | ( | 4.1% | ) | | 68 | ( | 23.4% | ) | | 51 | ( | 17.6% | ) | | 22 | ( | 7.6% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 3.1% | ) | | 4 | ( | 1.4% | ) | | 3 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor7
[factor] |
1. 23
2. 32
3. 33
4. 34
5. 35
6. 43
7. 44
8. 45
9. 53
10. 54
11. 55
12. 99
13. 998
14. 999 |
| 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 222 | ( | 20.5% | ) | | 307 | ( | 28.3% | ) | | 14 | ( | 1.3% | ) | | 242 | ( | 22.3% | ) | | 154 | ( | 14.2% | ) | | 75 | ( | 6.9% | ) | | 5 | ( | 0.5% | ) | | 28 | ( | 2.6% | ) | | 10 | ( | 0.9% | ) | | 6 | ( | 0.6% | ) | | 3 | ( | 0.3% | ) | | 15 | ( | 1.4% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor7
[factor] |
1. 23
2. 32
3. 33
4. 34
5. 35
6. 43
7. 44
8. 45
9. 53
10. 54
11. 55
12. 99
13. 998
14. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 37.5% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 8
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 8 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2862
All data
st_css() #IMPORTANT!
cssitespecificfactor8 <- as.factor(trimws(d[,"cssitespecificfactor8"]))
new.d <- data.frame(new.d, cssitespecificfactor8)
new.d <- apply_labels(new.d, cssitespecificfactor8 = "cs_site_specific_factor8")
temp.d <- data.frame (new.d.1, cssitespecificfactor8)
summarytools::view(dfSummary(new.d$cssitespecificfactor8, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor8
[labelled, factor] |
cs_site_specific_factor8 |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 21 | ( | 1.0% | ) | | 3 | ( | 0.1% | ) | | 488 | ( | 22.9% | ) | | 1073 | ( | 50.3% | ) | | 332 | ( | 15.6% | ) | | 192 | ( | 9.0% | ) | | 5 | ( | 0.2% | ) | | 18 | ( | 0.8% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor8
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 59 | ( | 31.2% | ) | | 92 | ( | 48.7% | ) | | 16 | ( | 8.5% | ) | | 18 | ( | 9.5% | ) | | 1 | ( | 0.5% | ) | | 3 | ( | 1.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor8
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 62 | ( | 35.6% | ) | | 88 | ( | 50.6% | ) | | 11 | ( | 6.3% | ) | | 9 | ( | 5.2% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor8
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 4 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 66 | ( | 27.8% | ) | | 104 | ( | 43.9% | ) | | 38 | ( | 16.0% | ) | | 21 | ( | 8.9% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 1.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor8
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 16.2% | ) | | 79 | ( | 55.6% | ) | | 29 | ( | 20.4% | ) | | 10 | ( | 7.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor8
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 4 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 45 | ( | 15.5% | ) | | 146 | ( | 50.3% | ) | | 63 | ( | 21.7% | ) | | 31 | ( | 10.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor8
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 11 | ( | 1.0% | ) | | 3 | ( | 0.3% | ) | | 226 | ( | 20.8% | ) | | 556 | ( | 51.3% | ) | | 174 | ( | 16.1% | ) | | 103 | ( | 9.5% | ) | | 3 | ( | 0.3% | ) | | 8 | ( | 0.7% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor8
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 43.8% | ) | | 8 | ( | 50.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 9
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 9 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2863
All data
st_css() #IMPORTANT!
cssitespecificfactor9 <- as.factor(trimws(d[,"cssitespecificfactor9"]))
new.d <- data.frame(new.d, cssitespecificfactor9)
new.d <- apply_labels(new.d, cssitespecificfactor9 = "cs_site_specific_factor9")
temp.d <- data.frame (new.d.1, cssitespecificfactor9)
summarytools::view(dfSummary(new.d$cssitespecificfactor9, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor9
[labelled, factor] |
cs_site_specific_factor9 |
1. 32
2. 33
3. 34
4. 35
5. 43
6. 44
7. 45
8. 53
9. 54
10. 55
11. 99
12. 998
13. 999 |
| 1 | ( | 0.0% | ) | | 143 | ( | 6.7% | ) | | 462 | ( | 21.7% | ) | | 10 | ( | 0.5% | ) | | 216 | ( | 10.1% | ) | | 44 | ( | 2.1% | ) | | 54 | ( | 2.5% | ) | | 2 | ( | 0.1% | ) | | 13 | ( | 0.6% | ) | | 5 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 1159 | ( | 54.3% | ) | | 23 | ( | 1.1% | ) |
|
 |
34
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor9
[factor] |
1. 32
2. 33
3. 34
4. 35
5. 43
6. 44
7. 45
8. 53
9. 54
10. 55
11. 99
12. 998
13. 999 |
| 0 | ( | 0.0% | ) | | 19 | ( | 10.1% | ) | | 43 | ( | 22.8% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 12.2% | ) | | 8 | ( | 4.2% | ) | | 6 | ( | 3.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 85 | ( | 45.0% | ) | | 3 | ( | 1.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor9
[factor] |
1. 32
2. 33
3. 34
4. 35
5. 43
6. 44
7. 45
8. 53
9. 54
10. 55
11. 99
12. 998
13. 999 |
| 0 | ( | 0.0% | ) | | 6 | ( | 3.4% | ) | | 23 | ( | 13.2% | ) | | 0 | ( | 0.0% | ) | | 13 | ( | 7.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 129 | ( | 74.1% | ) | | 2 | ( | 1.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor9
[factor] |
1. 32
2. 33
3. 34
4. 35
5. 43
6. 44
7. 45
8. 53
9. 54
10. 55
11. 99
12. 998
13. 999 |
| 0 | ( | 0.0% | ) | | 27 | ( | 11.4% | ) | | 60 | ( | 25.3% | ) | | 2 | ( | 0.8% | ) | | 37 | ( | 15.6% | ) | | 3 | ( | 1.3% | ) | | 6 | ( | 2.5% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 99 | ( | 41.8% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor9
[factor] |
1. 32
2. 33
3. 34
4. 35
5. 43
6. 44
7. 45
8. 53
9. 54
10. 55
11. 99
12. 998
13. 999 |
| 0 | ( | 0.0% | ) | | 10 | ( | 7.0% | ) | | 31 | ( | 21.8% | ) | | 1 | ( | 0.7% | ) | | 21 | ( | 14.8% | ) | | 1 | ( | 0.7% | ) | | 9 | ( | 6.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 64 | ( | 45.1% | ) | | 3 | ( | 2.1% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor9
[factor] |
1. 32
2. 33
3. 34
4. 35
5. 43
6. 44
7. 45
8. 53
9. 54
10. 55
11. 99
12. 998
13. 999 |
| 0 | ( | 0.0% | ) | | 26 | ( | 9.0% | ) | | 92 | ( | 31.7% | ) | | 3 | ( | 1.0% | ) | | 29 | ( | 10.0% | ) | | 15 | ( | 5.2% | ) | | 9 | ( | 3.1% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 108 | ( | 37.2% | ) | | 5 | ( | 1.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor9
[factor] |
1. 32
2. 33
3. 34
4. 35
5. 43
6. 44
7. 45
8. 53
9. 54
10. 55
11. 99
12. 998
13. 999 |
| 1 | ( | 0.1% | ) | | 54 | ( | 5.0% | ) | | 211 | ( | 19.4% | ) | | 4 | ( | 0.4% | ) | | 90 | ( | 8.3% | ) | | 17 | ( | 1.6% | ) | | 23 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 0.8% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 664 | ( | 61.2% | ) | | 9 | ( | 0.8% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor9
[factor] |
1. 32
2. 33
3. 34
4. 35
5. 43
6. 44
7. 45
8. 53
9. 54
10. 55
11. 99
12. 998
13. 999 |
| 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 2 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 62.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 10
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor10 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2864
All data
st_css() #IMPORTANT!
cssitespecificfactor10 <- as.factor(trimws(d[,"cssitespecificfactor10"]))
new.d <- data.frame(new.d, cssitespecificfactor10)
new.d <- apply_labels(new.d, cssitespecificfactor10 = "cs_site_specific_factor10")
temp.d <- data.frame (new.d.1, cssitespecificfactor10)
summarytools::view(dfSummary(new.d$cssitespecificfactor10, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor10
[labelled, factor] |
cs_site_specific_factor10 |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 5 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 145 | ( | 6.8% | ) | | 682 | ( | 32.0% | ) | | 56 | ( | 2.6% | ) | | 67 | ( | 3.1% | ) | | 1159 | ( | 54.3% | ) | | 18 | ( | 0.8% | ) |
|
 |
34
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor10
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 19 | ( | 10.1% | ) | | 66 | ( | 34.9% | ) | | 8 | ( | 4.2% | ) | | 8 | ( | 4.2% | ) | | 85 | ( | 45.0% | ) | | 3 | ( | 1.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor10
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.4% | ) | | 36 | ( | 20.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 129 | ( | 74.1% | ) | | 2 | ( | 1.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor10
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 27 | ( | 11.4% | ) | | 97 | ( | 40.9% | ) | | 6 | ( | 2.5% | ) | | 6 | ( | 2.5% | ) | | 99 | ( | 41.8% | ) | | 1 | ( | 0.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor10
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 7.0% | ) | | 52 | ( | 36.6% | ) | | 2 | ( | 1.4% | ) | | 10 | ( | 7.0% | ) | | 64 | ( | 45.1% | ) | | 3 | ( | 2.1% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor10
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 26 | ( | 9.0% | ) | | 121 | ( | 41.7% | ) | | 19 | ( | 6.6% | ) | | 10 | ( | 3.4% | ) | | 108 | ( | 37.2% | ) | | 5 | ( | 1.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor10
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 56 | ( | 5.2% | ) | | 305 | ( | 28.1% | ) | | 21 | ( | 1.9% | ) | | 32 | ( | 2.9% | ) | | 664 | ( | 61.2% | ) | | 4 | ( | 0.4% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor10
[factor] |
1. 10
2. 5
3. 6
4. 7
5. 8
6. 9
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 62.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 11
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor11 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2865
All data
st_css() #IMPORTANT!
cssitespecificfactor11 <- as.factor(trimws(d[,"cssitespecificfactor11"]))
new.d <- data.frame(new.d, cssitespecificfactor11)
new.d <- apply_labels(new.d, cssitespecificfactor11 = "cs_site_specific_factor11")
temp.d <- data.frame (new.d.1, cssitespecificfactor11)
summarytools::view(dfSummary(new.d$cssitespecificfactor11, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor11
[labelled, factor] |
cs_site_specific_factor11 |
1. 30
2. 40
3. 50
4. 988
5. 998
6. 999 |
| 22 | ( | 1.0% | ) | | 39 | ( | 1.8% | ) | | 113 | ( | 5.3% | ) | | 162 | ( | 7.6% | ) | | 1129 | ( | 52.9% | ) | | 668 | ( | 31.3% | ) |
|
 |
34
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor11
[factor] |
1. 30
2. 40
3. 50
4. 988
5. 998
6. 999 |
| 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 14 | ( | 7.4% | ) | | 14 | ( | 7.4% | ) | | 81 | ( | 42.9% | ) | | 77 | ( | 40.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor11
[factor] |
1. 30
2. 40
3. 50
4. 988
5. 998
6. 999 |
| 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 9 | ( | 5.2% | ) | | 3 | ( | 1.7% | ) | | 129 | ( | 74.1% | ) | | 30 | ( | 17.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor11
[factor] |
1. 30
2. 40
3. 50
4. 988
5. 998
6. 999 |
| 0 | ( | 0.0% | ) | | 7 | ( | 3.0% | ) | | 18 | ( | 7.6% | ) | | 16 | ( | 6.8% | ) | | 106 | ( | 44.7% | ) | | 90 | ( | 38.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor11
[factor] |
1. 30
2. 40
3. 50
4. 988
5. 998
6. 999 |
| 3 | ( | 2.1% | ) | | 1 | ( | 0.7% | ) | | 8 | ( | 5.6% | ) | | 45 | ( | 31.7% | ) | | 43 | ( | 30.3% | ) | | 42 | ( | 29.6% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor11
[factor] |
1. 30
2. 40
3. 50
4. 988
5. 998
6. 999 |
| 7 | ( | 2.4% | ) | | 8 | ( | 2.8% | ) | | 26 | ( | 9.0% | ) | | 10 | ( | 3.4% | ) | | 108 | ( | 37.2% | ) | | 131 | ( | 45.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor11
[factor] |
1. 30
2. 40
3. 50
4. 988
5. 998
6. 999 |
| 11 | ( | 1.0% | ) | | 18 | ( | 1.7% | ) | | 38 | ( | 3.5% | ) | | 71 | ( | 6.5% | ) | | 654 | ( | 60.3% | ) | | 293 | ( | 27.0% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor11
[factor] |
1. 30
2. 40
3. 50
4. 988
5. 998
6. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) | | 8 | ( | 50.0% | ) | | 5 | ( | 31.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 12
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor12 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2866
All data
st_css() #IMPORTANT!
cssitespecificfactor12 <- as.factor(trimws(d[,"cssitespecificfactor12"]))
new.d <- data.frame(new.d, cssitespecificfactor12)
new.d <- apply_labels(new.d, cssitespecificfactor12 = "cs_site_specific_factor12")
temp.d <- data.frame (new.d.1, cssitespecificfactor12)
summarytools::view(dfSummary(new.d$cssitespecificfactor12, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor12
[labelled, factor] |
cs_site_specific_factor12 |
1. 0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 23
17. 26
18. 3
19. 35
20. 38
21. 4
22. 5
23. 6
24. 7
25. 8
26. 9
27. 991
28. 998
29. 999 |
| 4 | ( | 0.2% | ) | | 218 | ( | 10.2% | ) | | 77 | ( | 3.6% | ) | | 66 | ( | 3.1% | ) | | 76 | ( | 3.6% | ) | | 12 | ( | 0.6% | ) | | 13 | ( | 0.6% | ) | | 6 | ( | 0.3% | ) | | 6 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 213 | ( | 10.0% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 216 | ( | 10.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 203 | ( | 9.5% | ) | | 193 | ( | 9.1% | ) | | 179 | ( | 8.4% | ) | | 100 | ( | 4.7% | ) | | 97 | ( | 4.5% | ) | | 73 | ( | 3.4% | ) | | 311 | ( | 14.6% | ) | | 18 | ( | 0.8% | ) | | 37 | ( | 1.7% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor12
[factor] |
1. 0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 23
17. 26
18. 3
19. 35
20. 38
21. 4
22. 5
23. 6
24. 7
25. 8
26. 9
27. 991
28. 998
29. 999 |
| 0 | ( | 0.0% | ) | | 21 | ( | 11.1% | ) | | 2 | ( | 1.1% | ) | | 5 | ( | 2.6% | ) | | 3 | ( | 1.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 15 | ( | 7.9% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 25 | ( | 13.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 16 | ( | 8.5% | ) | | 11 | ( | 5.8% | ) | | 11 | ( | 5.8% | ) | | 9 | ( | 4.8% | ) | | 11 | ( | 5.8% | ) | | 7 | ( | 3.7% | ) | | 38 | ( | 20.1% | ) | | 3 | ( | 1.6% | ) | | 4 | ( | 2.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor12
[factor] |
1. 0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 23
17. 26
18. 3
19. 35
20. 38
21. 4
22. 5
23. 6
24. 7
25. 8
26. 9
27. 991
28. 998
29. 999 |
| 1 | ( | 0.6% | ) | | 27 | ( | 15.5% | ) | | 6 | ( | 3.4% | ) | | 2 | ( | 1.1% | ) | | 6 | ( | 3.4% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 18 | ( | 10.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 21 | ( | 12.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 20 | ( | 11.5% | ) | | 16 | ( | 9.2% | ) | | 10 | ( | 5.7% | ) | | 12 | ( | 6.9% | ) | | 5 | ( | 2.9% | ) | | 7 | ( | 4.0% | ) | | 15 | ( | 8.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor12
[factor] |
1. 0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 23
17. 26
18. 3
19. 35
20. 38
21. 4
22. 5
23. 6
24. 7
25. 8
26. 9
27. 991
28. 998
29. 999 |
| 1 | ( | 0.4% | ) | | 21 | ( | 8.9% | ) | | 14 | ( | 5.9% | ) | | 14 | ( | 5.9% | ) | | 7 | ( | 3.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 17 | ( | 7.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 16 | ( | 6.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 25 | ( | 10.5% | ) | | 21 | ( | 8.9% | ) | | 25 | ( | 10.5% | ) | | 10 | ( | 4.2% | ) | | 11 | ( | 4.6% | ) | | 7 | ( | 3.0% | ) | | 39 | ( | 16.5% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor12
[factor] |
1. 0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 23
17. 26
18. 3
19. 35
20. 38
21. 4
22. 5
23. 6
24. 7
25. 8
26. 9
27. 991
28. 998
29. 999 |
| 0 | ( | 0.0% | ) | | 15 | ( | 10.6% | ) | | 6 | ( | 4.2% | ) | | 3 | ( | 2.1% | ) | | 4 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 14 | ( | 9.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 14 | ( | 9.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 6.3% | ) | | 20 | ( | 14.1% | ) | | 22 | ( | 15.5% | ) | | 12 | ( | 8.5% | ) | | 7 | ( | 4.9% | ) | | 4 | ( | 2.8% | ) | | 10 | ( | 7.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor12
[factor] |
1. 0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 23
17. 26
18. 3
19. 35
20. 38
21. 4
22. 5
23. 6
24. 7
25. 8
26. 9
27. 991
28. 998
29. 999 |
| 1 | ( | 0.3% | ) | | 22 | ( | 7.6% | ) | | 10 | ( | 3.4% | ) | | 11 | ( | 3.8% | ) | | 7 | ( | 2.4% | ) | | 3 | ( | 1.0% | ) | | 3 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 28 | ( | 9.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 27 | ( | 9.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 27 | ( | 9.3% | ) | | 30 | ( | 10.3% | ) | | 33 | ( | 11.4% | ) | | 13 | ( | 4.5% | ) | | 18 | ( | 6.2% | ) | | 4 | ( | 1.4% | ) | | 46 | ( | 15.9% | ) | | 3 | ( | 1.0% | ) | | 3 | ( | 1.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor12
[factor] |
1. 0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 23
17. 26
18. 3
19. 35
20. 38
21. 4
22. 5
23. 6
24. 7
25. 8
26. 9
27. 991
28. 998
29. 999 |
| 1 | ( | 0.1% | ) | | 109 | ( | 10.1% | ) | | 39 | ( | 3.6% | ) | | 31 | ( | 2.9% | ) | | 49 | ( | 4.5% | ) | | 5 | ( | 0.5% | ) | | 8 | ( | 0.7% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 117 | ( | 10.8% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 113 | ( | 10.4% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 106 | ( | 9.8% | ) | | 95 | ( | 8.8% | ) | | 77 | ( | 7.1% | ) | | 43 | ( | 4.0% | ) | | 45 | ( | 4.2% | ) | | 42 | ( | 3.9% | ) | | 158 | ( | 14.6% | ) | | 8 | ( | 0.7% | ) | | 26 | ( | 2.4% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor12
[factor] |
1. 0
2. 1
3. 10
4. 11
5. 12
6. 13
7. 14
8. 15
9. 16
10. 17
11. 18
12. 19
13. 2
14. 20
15. 21
16. 23
17. 26
18. 3
19. 35
20. 38
21. 4
22. 5
23. 6
24. 7
25. 8
26. 9
27. 991
28. 998
29. 999 |
| 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 12.5% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 13
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor13 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2867
All data
st_css() #IMPORTANT!
cssitespecificfactor13 <- as.factor(trimws(d[,"cssitespecificfactor13"]))
new.d <- data.frame(new.d, cssitespecificfactor13)
new.d <- apply_labels(new.d, cssitespecificfactor13 = "cs_site_specific_factor13")
temp.d <- data.frame (new.d.1, cssitespecificfactor13)
summarytools::view(dfSummary(new.d$cssitespecificfactor13, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor13
[labelled, factor] |
cs_site_specific_factor13 |
1. 1
2. 10
3. 11
4. 12
5. 13
6. 14
7. 15
8. 16
9. 17
10. 18
11. 19
12. 2
13. 20
14. 21
15. 22
16. 23
17. 24
18. 25
19. 26
20. 27
21. 28
22. 29
23. 3
24. 30
25. 31
26. 32
27. 33
28. 34
29. 35
30. 36
31. 37
32. 38
33. 4
34. 40
35. 41
36. 42
37. 43
38. 47
39. 48
40. 5
41. 6
42. 7
43. 8
44. 9
45. 991
46. 998
47. 999 |
| 3 | ( | 0.1% | ) | | 34 | ( | 1.6% | ) | | 22 | ( | 1.0% | ) | | 994 | ( | 46.6% | ) | | 65 | ( | 3.0% | ) | | 172 | ( | 8.1% | ) | | 49 | ( | 2.3% | ) | | 58 | ( | 2.7% | ) | | 24 | ( | 1.1% | ) | | 30 | ( | 1.4% | ) | | 20 | ( | 0.9% | ) | | 7 | ( | 0.3% | ) | | 8 | ( | 0.4% | ) | | 9 | ( | 0.4% | ) | | 6 | ( | 0.3% | ) | | 3 | ( | 0.1% | ) | | 5 | ( | 0.2% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 7 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 16 | ( | 0.8% | ) | | 61 | ( | 2.9% | ) | | 12 | ( | 0.6% | ) | | 21 | ( | 1.0% | ) | | 8 | ( | 0.4% | ) | | 399 | ( | 18.7% | ) | | 18 | ( | 0.8% | ) | | 36 | ( | 1.7% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor13
[factor] |
1. 1
2. 10
3. 11
4. 12
5. 13
6. 14
7. 15
8. 16
9. 17
10. 18
11. 19
12. 2
13. 20
14. 21
15. 22
16. 23
17. 24
18. 25
19. 26
20. 27
21. 28
22. 29
23. 3
24. 30
25. 31
26. 32
27. 33
28. 34
29. 35
30. 36
31. 37
32. 38
33. 4
34. 40
35. 41
36. 42
37. 43
38. 47
39. 48
40. 5
41. 6
42. 7
43. 8
44. 9
45. 991
46. 998
47. 999 |
| 1 | ( | 0.5% | ) | | 4 | ( | 2.1% | ) | | 1 | ( | 0.5% | ) | | 69 | ( | 36.5% | ) | | 5 | ( | 2.6% | ) | | 12 | ( | 6.3% | ) | | 2 | ( | 1.1% | ) | | 6 | ( | 3.2% | ) | | 3 | ( | 1.6% | ) | | 1 | ( | 0.5% | ) | | 3 | ( | 1.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 8 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.6% | ) | | 1 | ( | 0.5% | ) | | 52 | ( | 27.5% | ) | | 3 | ( | 1.6% | ) | | 4 | ( | 2.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor13
[factor] |
1. 1
2. 10
3. 11
4. 12
5. 13
6. 14
7. 15
8. 16
9. 17
10. 18
11. 19
12. 2
13. 20
14. 21
15. 22
16. 23
17. 24
18. 25
19. 26
20. 27
21. 28
22. 29
23. 3
24. 30
25. 31
26. 32
27. 33
28. 34
29. 35
30. 36
31. 37
32. 38
33. 4
34. 40
35. 41
36. 42
37. 43
38. 47
39. 48
40. 5
41. 6
42. 7
43. 8
44. 9
45. 991
46. 998
47. 999 |
| 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 68 | ( | 39.1% | ) | | 4 | ( | 2.3% | ) | | 35 | ( | 20.1% | ) | | 7 | ( | 4.0% | ) | | 10 | ( | 5.7% | ) | | 6 | ( | 3.4% | ) | | 5 | ( | 2.9% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 4 | ( | 2.3% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 15 | ( | 8.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor13
[factor] |
1. 1
2. 10
3. 11
4. 12
5. 13
6. 14
7. 15
8. 16
9. 17
10. 18
11. 19
12. 2
13. 20
14. 21
15. 22
16. 23
17. 24
18. 25
19. 26
20. 27
21. 28
22. 29
23. 3
24. 30
25. 31
26. 32
27. 33
28. 34
29. 35
30. 36
31. 37
32. 38
33. 4
34. 40
35. 41
36. 42
37. 43
38. 47
39. 48
40. 5
41. 6
42. 7
43. 8
44. 9
45. 991
46. 998
47. 999 |
| 0 | ( | 0.0% | ) | | 5 | ( | 2.1% | ) | | 3 | ( | 1.3% | ) | | 106 | ( | 44.7% | ) | | 6 | ( | 2.5% | ) | | 21 | ( | 8.9% | ) | | 9 | ( | 3.8% | ) | | 8 | ( | 3.4% | ) | | 3 | ( | 1.3% | ) | | 4 | ( | 1.7% | ) | | 2 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 6 | ( | 2.5% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 56 | ( | 23.6% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor13
[factor] |
1. 1
2. 10
3. 11
4. 12
5. 13
6. 14
7. 15
8. 16
9. 17
10. 18
11. 19
12. 2
13. 20
14. 21
15. 22
16. 23
17. 24
18. 25
19. 26
20. 27
21. 28
22. 29
23. 3
24. 30
25. 31
26. 32
27. 33
28. 34
29. 35
30. 36
31. 37
32. 38
33. 4
34. 40
35. 41
36. 42
37. 43
38. 47
39. 48
40. 5
41. 6
42. 7
43. 8
44. 9
45. 991
46. 998
47. 999 |
| 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 5 | ( | 3.5% | ) | | 70 | ( | 49.3% | ) | | 11 | ( | 7.7% | ) | | 8 | ( | 5.6% | ) | | 2 | ( | 1.4% | ) | | 4 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 32 | ( | 22.5% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor13
[factor] |
1. 1
2. 10
3. 11
4. 12
5. 13
6. 14
7. 15
8. 16
9. 17
10. 18
11. 19
12. 2
13. 20
14. 21
15. 22
16. 23
17. 24
18. 25
19. 26
20. 27
21. 28
22. 29
23. 3
24. 30
25. 31
26. 32
27. 33
28. 34
29. 35
30. 36
31. 37
32. 38
33. 4
34. 40
35. 41
36. 42
37. 43
38. 47
39. 48
40. 5
41. 6
42. 7
43. 8
44. 9
45. 991
46. 998
47. 999 |
| 0 | ( | 0.0% | ) | | 5 | ( | 1.7% | ) | | 2 | ( | 0.7% | ) | | 101 | ( | 34.8% | ) | | 2 | ( | 0.7% | ) | | 47 | ( | 16.2% | ) | | 4 | ( | 1.4% | ) | | 3 | ( | 1.0% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 4.1% | ) | | 5 | ( | 1.7% | ) | | 7 | ( | 2.4% | ) | | 4 | ( | 1.4% | ) | | 83 | ( | 28.6% | ) | | 3 | ( | 1.0% | ) | | 3 | ( | 1.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor13
[factor] |
1. 1
2. 10
3. 11
4. 12
5. 13
6. 14
7. 15
8. 16
9. 17
10. 18
11. 19
12. 2
13. 20
14. 21
15. 22
16. 23
17. 24
18. 25
19. 26
20. 27
21. 28
22. 29
23. 3
24. 30
25. 31
26. 32
27. 33
28. 34
29. 35
30. 36
31. 37
32. 38
33. 4
34. 40
35. 41
36. 42
37. 43
38. 47
39. 48
40. 5
41. 6
42. 7
43. 8
44. 9
45. 991
46. 998
47. 999 |
| 2 | ( | 0.2% | ) | | 16 | ( | 1.5% | ) | | 9 | ( | 0.8% | ) | | 575 | ( | 53.0% | ) | | 37 | ( | 3.4% | ) | | 49 | ( | 4.5% | ) | | 25 | ( | 2.3% | ) | | 27 | ( | 2.5% | ) | | 10 | ( | 0.9% | ) | | 16 | ( | 1.5% | ) | | 11 | ( | 1.0% | ) | | 5 | ( | 0.5% | ) | | 4 | ( | 0.4% | ) | | 6 | ( | 0.6% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 7 | ( | 0.6% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 6 | ( | 0.6% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 12 | ( | 1.1% | ) | | 31 | ( | 2.9% | ) | | 4 | ( | 0.4% | ) | | 10 | ( | 0.9% | ) | | 1 | ( | 0.1% | ) | | 150 | ( | 13.8% | ) | | 8 | ( | 0.7% | ) | | 24 | ( | 2.2% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor13
[factor] |
1. 1
2. 10
3. 11
4. 12
5. 13
6. 14
7. 15
8. 16
9. 17
10. 18
11. 19
12. 2
13. 20
14. 21
15. 22
16. 23
17. 24
18. 25
19. 26
20. 27
21. 28
22. 29
23. 3
24. 30
25. 31
26. 32
27. 33
28. 34
29. 35
30. 36
31. 37
32. 38
33. 4
34. 40
35. 41
36. 42
37. 43
38. 47
39. 48
40. 5
41. 6
42. 7
43. 8
44. 9
45. 991
46. 998
47. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 68.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 14
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor14 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2868
All data
st_css() #IMPORTANT!
cssitespecificfactor14 <- as.factor(trimws(d[,"cssitespecificfactor14"]))
new.d <- data.frame(new.d, cssitespecificfactor14)
new.d <- apply_labels(new.d, cssitespecificfactor14 = "cs_site_specific_factor14")
temp.d <- data.frame (new.d.1, cssitespecificfactor14)
summarytools::view(dfSummary(new.d$cssitespecificfactor14, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor14
[labelled, factor] |
cs_site_specific_factor14 |
1. 0
2. 10
3. 20
4. 30
5. 50
6. 988
7. 998
8. 999 |
| 2 | ( | 0.1% | ) | | 489 | ( | 22.9% | ) | | 707 | ( | 33.2% | ) | | 105 | ( | 4.9% | ) | | 2 | ( | 0.1% | ) | | 745 | ( | 34.9% | ) | | 18 | ( | 0.8% | ) | | 64 | ( | 3.0% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor14
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 50
6. 988
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 58 | ( | 30.7% | ) | | 82 | ( | 43.4% | ) | | 15 | ( | 7.9% | ) | | 0 | ( | 0.0% | ) | | 32 | ( | 16.9% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor14
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 50
6. 988
7. 998
8. 999 |
| 1 | ( | 0.6% | ) | | 76 | ( | 43.7% | ) | | 77 | ( | 44.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 17 | ( | 9.8% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor14
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 50
6. 988
7. 998
8. 999 |
| 1 | ( | 0.4% | ) | | 78 | ( | 32.9% | ) | | 112 | ( | 47.3% | ) | | 18 | ( | 7.6% | ) | | 2 | ( | 0.8% | ) | | 20 | ( | 8.4% | ) | | 2 | ( | 0.8% | ) | | 4 | ( | 1.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor14
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 50
6. 988
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 7 | ( | 4.9% | ) | | 18 | ( | 12.7% | ) | | 7 | ( | 4.9% | ) | | 0 | ( | 0.0% | ) | | 107 | ( | 75.4% | ) | | 2 | ( | 1.4% | ) | | 1 | ( | 0.7% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor14
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 50
6. 988
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 81 | ( | 27.9% | ) | | 128 | ( | 44.1% | ) | | 18 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 47 | ( | 16.2% | ) | | 4 | ( | 1.4% | ) | | 12 | ( | 4.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor14
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 50
6. 988
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 186 | ( | 17.2% | ) | | 288 | ( | 26.6% | ) | | 47 | ( | 4.3% | ) | | 0 | ( | 0.0% | ) | | 511 | ( | 47.1% | ) | | 6 | ( | 0.6% | ) | | 46 | ( | 4.2% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor14
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 50
6. 988
7. 998
8. 999 |
| 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) | | 2 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 68.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 15
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor15 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2869
All data
st_css() #IMPORTANT!
cssitespecificfactor15 <- as.factor(trimws(d[,"cssitespecificfactor15"]))
new.d <- data.frame(new.d, cssitespecificfactor15)
new.d <- apply_labels(new.d, cssitespecificfactor15 = "cs_site_specific_factor15")
temp.d <- data.frame (new.d.1, cssitespecificfactor15)
summarytools::view(dfSummary(new.d$cssitespecificfactor15, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor15
[labelled, factor] |
cs_site_specific_factor15 |
1. 0
2. 10
3. 20
4. 30
5. 988
6. 999 |
| 30 | ( | 1.4% | ) | | 167 | ( | 7.8% | ) | | 177 | ( | 8.3% | ) | | 734 | ( | 34.4% | ) | | 752 | ( | 35.3% | ) | | 272 | ( | 12.8% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor15
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 988
6. 999 |
| 3 | ( | 1.6% | ) | | 14 | ( | 7.4% | ) | | 13 | ( | 6.9% | ) | | 105 | ( | 55.6% | ) | | 31 | ( | 16.4% | ) | | 23 | ( | 12.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor15
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 988
6. 999 |
| 2 | ( | 1.1% | ) | | 33 | ( | 19.0% | ) | | 18 | ( | 10.3% | ) | | 99 | ( | 56.9% | ) | | 17 | ( | 9.8% | ) | | 5 | ( | 2.9% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor15
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 988
6. 999 |
| 4 | ( | 1.7% | ) | | 24 | ( | 10.1% | ) | | 24 | ( | 10.1% | ) | | 144 | ( | 60.8% | ) | | 21 | ( | 8.9% | ) | | 20 | ( | 8.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor15
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 988
6. 999 |
| 0 | ( | 0.0% | ) | | 4 | ( | 2.8% | ) | | 2 | ( | 1.4% | ) | | 13 | ( | 9.2% | ) | | 112 | ( | 78.9% | ) | | 11 | ( | 7.7% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor15
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 988
6. 999 |
| 9 | ( | 3.1% | ) | | 26 | ( | 9.0% | ) | | 26 | ( | 9.0% | ) | | 145 | ( | 50.0% | ) | | 50 | ( | 17.2% | ) | | 34 | ( | 11.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor15
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 988
6. 999 |
| 12 | ( | 1.1% | ) | | 66 | ( | 6.1% | ) | | 94 | ( | 8.7% | ) | | 225 | ( | 20.8% | ) | | 509 | ( | 47.0% | ) | | 178 | ( | 16.4% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor15
[factor] |
1. 0
2. 10
3. 20
4. 30
5. 988
6. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 18.8% | ) | | 12 | ( | 75.0% | ) | | 1 | ( | 6.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 1
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 1 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2880
All data
st_css() #IMPORTANT!
cssitespecificfactor1 <- as.factor(trimws(d[,"cssitespecificfactor1"]))
new.d <- data.frame(new.d, cssitespecificfactor1)
new.d <- apply_labels(new.d, cssitespecificfactor1 = "cs_site_specific_factor1")
temp.d <- data.frame (new.d.1, cssitespecificfactor1)
summarytools::view(dfSummary(new.d$cssitespecificfactor1, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor1
[labelled, factor] |
cs_site_specific_factor1 |
1. 1
2. 10
3. 100
4. 101
5. 102
6. 103
7. 104
8. 105
9. 106
10. 107
11. 108
12. 109
13. 11
14. 110
15. 111
16. 112
17. 113
18. 114
19. 115
20. 116
21. 117
22. 118
23. 119
24. 120
25. 121
26. 122
27. 123
28. 124
29. 125
30. 126
31. 127
32. 128
33. 129
34. 13
35. 130
36. 131
37. 132
38. 133
39. 134
40. 135
41. 136
42. 137
43. 138
44. 139
45. 14
46. 140
47. 141
48. 142
49. 143
50. 144
[ 308 others ] |
| 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 14 | ( | 0.7% | ) | | 9 | ( | 0.4% | ) | | 8 | ( | 0.4% | ) | | 10 | ( | 0.5% | ) | | 5 | ( | 0.2% | ) | | 12 | ( | 0.6% | ) | | 8 | ( | 0.4% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 7 | ( | 0.3% | ) | | 6 | ( | 0.3% | ) | | 5 | ( | 0.2% | ) | | 6 | ( | 0.3% | ) | | 6 | ( | 0.3% | ) | | 4 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 6 | ( | 0.3% | ) | | 11 | ( | 0.5% | ) | | 6 | ( | 0.3% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 7 | ( | 0.3% | ) | | 6 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 6 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 7 | ( | 0.3% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 6 | ( | 0.3% | ) | | 4 | ( | 0.2% | ) | | 5 | ( | 0.2% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 4 | ( | 0.2% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 1885 | ( | 88.4% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor1
[factor] |
1. 1
2. 10
3. 100
4. 101
5. 102
6. 103
7. 104
8. 105
9. 106
10. 107
11. 108
12. 109
13. 11
14. 110
15. 111
16. 112
17. 113
18. 114
19. 115
20. 116
21. 117
22. 118
23. 119
24. 120
25. 121
26. 122
27. 123
28. 124
29. 125
30. 126
31. 127
32. 128
33. 129
34. 13
35. 130
36. 131
37. 132
38. 133
39. 134
40. 135
41. 136
42. 137
43. 138
44. 139
45. 14
46. 140
47. 141
48. 142
49. 143
50. 144
[ 308 others ] |
| 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 3 | ( | 1.6% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 161 | ( | 85.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor1
[factor] |
1. 1
2. 10
3. 100
4. 101
5. 102
6. 103
7. 104
8. 105
9. 106
10. 107
11. 108
12. 109
13. 11
14. 110
15. 111
16. 112
17. 113
18. 114
19. 115
20. 116
21. 117
22. 118
23. 119
24. 120
25. 121
26. 122
27. 123
28. 124
29. 125
30. 126
31. 127
32. 128
33. 129
34. 13
35. 130
36. 131
37. 132
38. 133
39. 134
40. 135
41. 136
42. 137
43. 138
44. 139
45. 14
46. 140
47. 141
48. 142
49. 143
50. 144
[ 308 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 155 | ( | 89.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor1
[factor] |
1. 1
2. 10
3. 100
4. 101
5. 102
6. 103
7. 104
8. 105
9. 106
10. 107
11. 108
12. 109
13. 11
14. 110
15. 111
16. 112
17. 113
18. 114
19. 115
20. 116
21. 117
22. 118
23. 119
24. 120
25. 121
26. 122
27. 123
28. 124
29. 125
30. 126
31. 127
32. 128
33. 129
34. 13
35. 130
36. 131
37. 132
38. 133
39. 134
40. 135
41. 136
42. 137
43. 138
44. 139
45. 14
46. 140
47. 141
48. 142
49. 143
50. 144
[ 308 others ] |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.8% | ) | | 2 | ( | 0.8% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 209 | ( | 88.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor1
[factor] |
1. 1
2. 10
3. 100
4. 101
5. 102
6. 103
7. 104
8. 105
9. 106
10. 107
11. 108
12. 109
13. 11
14. 110
15. 111
16. 112
17. 113
18. 114
19. 115
20. 116
21. 117
22. 118
23. 119
24. 120
25. 121
26. 122
27. 123
28. 124
29. 125
30. 126
31. 127
32. 128
33. 129
34. 13
35. 130
36. 131
37. 132
38. 133
39. 134
40. 135
41. 136
42. 137
43. 138
44. 139
45. 14
46. 140
47. 141
48. 142
49. 143
50. 144
[ 308 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 131 | ( | 92.3% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor1
[factor] |
1. 1
2. 10
3. 100
4. 101
5. 102
6. 103
7. 104
8. 105
9. 106
10. 107
11. 108
12. 109
13. 11
14. 110
15. 111
16. 112
17. 113
18. 114
19. 115
20. 116
21. 117
22. 118
23. 119
24. 120
25. 121
26. 122
27. 123
28. 124
29. 125
30. 126
31. 127
32. 128
33. 129
34. 13
35. 130
36. 131
37. 132
38. 133
39. 134
40. 135
41. 136
42. 137
43. 138
44. 139
45. 14
46. 140
47. 141
48. 142
49. 143
50. 144
[ 308 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 261 | ( | 90.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor1
[factor] |
1. 1
2. 10
3. 100
4. 101
5. 102
6. 103
7. 104
8. 105
9. 106
10. 107
11. 108
12. 109
13. 11
14. 110
15. 111
16. 112
17. 113
18. 114
19. 115
20. 116
21. 117
22. 118
23. 119
24. 120
25. 121
26. 122
27. 123
28. 124
29. 125
30. 126
31. 127
32. 128
33. 129
34. 13
35. 130
36. 131
37. 132
38. 133
39. 134
40. 135
41. 136
42. 137
43. 138
44. 139
45. 14
46. 140
47. 141
48. 142
49. 143
50. 144
[ 308 others ] |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 9 | ( | 0.8% | ) | | 4 | ( | 0.4% | ) | | 4 | ( | 0.4% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 8 | ( | 0.7% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 3 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 4 | ( | 0.4% | ) | | 8 | ( | 0.7% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 5 | ( | 0.5% | ) | | 4 | ( | 0.4% | ) | | 4 | ( | 0.4% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 954 | ( | 88.0% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor1
[factor] |
1. 1
2. 10
3. 100
4. 101
5. 102
6. 103
7. 104
8. 105
9. 106
10. 107
11. 108
12. 109
13. 11
14. 110
15. 111
16. 112
17. 113
18. 114
19. 115
20. 116
21. 117
22. 118
23. 119
24. 120
25. 121
26. 122
27. 123
28. 124
29. 125
30. 126
31. 127
32. 128
33. 129
34. 13
35. 130
36. 131
37. 132
38. 133
39. 134
40. 135
41. 136
42. 137
43. 138
44. 139
45. 14
46. 140
47. 141
48. 142
49. 143
50. 144
[ 308 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 14 | ( | 87.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 2
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 2 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2890
All data
st_css() #IMPORTANT!
cssitespecificfactor2 <- as.factor(trimws(d[,"cssitespecificfactor2"]))
new.d <- data.frame(new.d, cssitespecificfactor2)
new.d <- apply_labels(new.d, cssitespecificfactor2 = "cs_site_specific_factor2")
temp.d <- data.frame (new.d.1, cssitespecificfactor2)
summarytools::view(dfSummary(new.d$cssitespecificfactor2, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor2
[labelled, factor] |
cs_site_specific_factor2 |
1. 10
2. 20
3. 30
4. 997
5. 998
6. 999 |
| 1989 | ( | 93.3% | ) | | 54 | ( | 2.5% | ) | | 5 | ( | 0.2% | ) | | 9 | ( | 0.4% | ) | | 7 | ( | 0.3% | ) | | 68 | ( | 3.2% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor2
[factor] |
1. 10
2. 20
3. 30
4. 997
5. 998
6. 999 |
| 173 | ( | 91.5% | ) | | 9 | ( | 4.8% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 3.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor2
[factor] |
1. 10
2. 20
3. 30
4. 997
5. 998
6. 999 |
| 162 | ( | 93.1% | ) | | 7 | ( | 4.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor2
[factor] |
1. 10
2. 20
3. 30
4. 997
5. 998
6. 999 |
| 217 | ( | 91.6% | ) | | 7 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.8% | ) | | 10 | ( | 4.2% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor2
[factor] |
1. 10
2. 20
3. 30
4. 997
5. 998
6. 999 |
| 130 | ( | 91.5% | ) | | 4 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 3.5% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor2
[factor] |
1. 10
2. 20
3. 30
4. 997
5. 998
6. 999 |
| 278 | ( | 95.9% | ) | | 7 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 4 | ( | 1.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor2
[factor] |
1. 10
2. 20
3. 30
4. 997
5. 998
6. 999 |
| 1016 | ( | 93.7% | ) | | 20 | ( | 1.8% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 3 | ( | 0.3% | ) | | 39 | ( | 3.6% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor2
[factor] |
1. 10
2. 20
3. 30
4. 997
5. 998
6. 999 |
| 13 | ( | 81.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 12.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 3
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 3 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2900
All data
st_css() #IMPORTANT!
cssitespecificfactor3 <- as.factor(trimws(d[,"cssitespecificfactor3"]))
new.d <- data.frame(new.d, cssitespecificfactor3)
new.d <- apply_labels(new.d, cssitespecificfactor3 = "cs_site_specific_factor3")
temp.d <- data.frame (new.d.1, cssitespecificfactor3)
summarytools::view(dfSummary(new.d$cssitespecificfactor3, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor3
[labelled, factor] |
cs_site_specific_factor3 |
1. 0
2. 200
3. 210
4. 220
5. 230
6. 300
7. 320
8. 330
9. 340
10. 350
11. 400
12. 402
13. 404
14. 406
15. 415
16. 420
17. 430
18. 480
19. 482
20. 483
21. 485
22. 490
23. 495
24. 500
25. 950
26. 960
27. 970
28. 980
29. 990 |
| 1 | ( | 0.0% | ) | | 5 | ( | 0.2% | ) | | 45 | ( | 2.1% | ) | | 11 | ( | 0.5% | ) | | 499 | ( | 23.4% | ) | | 40 | ( | 1.9% | ) | | 2 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 11 | ( | 0.5% | ) | | 6 | ( | 0.3% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 51 | ( | 2.4% | ) | | 41 | ( | 1.9% | ) | | 54 | ( | 2.5% | ) | | 9 | ( | 0.4% | ) | | 34 | ( | 1.6% | ) | | 20 | ( | 0.9% | ) | | 11 | ( | 0.5% | ) | | 100 | ( | 4.7% | ) | | 11 | ( | 0.5% | ) | | 2 | ( | 0.1% | ) | | 3 | ( | 0.1% | ) | | 2 | ( | 0.1% | ) | | 25 | ( | 1.2% | ) | | 1134 | ( | 53.2% | ) | | 5 | ( | 0.2% | ) | | 6 | ( | 0.3% | ) |
|
 |
34
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor3
[factor] |
1. 0
2. 200
3. 210
4. 220
5. 230
6. 300
7. 320
8. 330
9. 340
10. 350
11. 400
12. 402
13. 404
14. 406
15. 415
16. 420
17. 430
18. 480
19. 482
20. 483
21. 485
22. 490
23. 495
24. 500
25. 950
26. 960
27. 970
28. 980
29. 990 |
| 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 4 | ( | 2.1% | ) | | 3 | ( | 1.6% | ) | | 53 | ( | 28.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 6 | ( | 3.2% | ) | | 6 | ( | 3.2% | ) | | 7 | ( | 3.7% | ) | | 1 | ( | 0.5% | ) | | 3 | ( | 1.6% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 5.8% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 83 | ( | 43.9% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor3
[factor] |
1. 0
2. 200
3. 210
4. 220
5. 230
6. 300
7. 320
8. 330
9. 340
10. 350
11. 400
12. 402
13. 404
14. 406
15. 415
16. 420
17. 430
18. 480
19. 482
20. 483
21. 485
22. 490
23. 495
24. 500
25. 950
26. 960
27. 970
28. 980
29. 990 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 14 | ( | 8.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 4 | ( | 2.3% | ) | | 5 | ( | 2.9% | ) | | 1 | ( | 0.6% | ) | | 3 | ( | 1.7% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 129 | ( | 74.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor3
[factor] |
1. 0
2. 200
3. 210
4. 220
5. 230
6. 300
7. 320
8. 330
9. 340
10. 350
11. 400
12. 402
13. 404
14. 406
15. 415
16. 420
17. 430
18. 480
19. 482
20. 483
21. 485
22. 490
23. 495
24. 500
25. 950
26. 960
27. 970
28. 980
29. 990 |
| 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) | | 5 | ( | 2.1% | ) | | 1 | ( | 0.4% | ) | | 73 | ( | 30.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 5.1% | ) | | 9 | ( | 3.8% | ) | | 7 | ( | 3.0% | ) | | 2 | ( | 0.8% | ) | | 6 | ( | 2.5% | ) | | 2 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 4.2% | ) | | 3 | ( | 1.3% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.3% | ) | | 96 | ( | 40.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor3
[factor] |
1. 0
2. 200
3. 210
4. 220
5. 230
6. 300
7. 320
8. 330
9. 340
10. 350
11. 400
12. 402
13. 404
14. 406
15. 415
16. 420
17. 430
18. 480
19. 482
20. 483
21. 485
22. 490
23. 495
24. 500
25. 950
26. 960
27. 970
28. 980
29. 990 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.1% | ) | | 1 | ( | 0.7% | ) | | 33 | ( | 23.2% | ) | | 3 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 4.2% | ) | | 1 | ( | 0.7% | ) | | 10 | ( | 7.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 8.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 66 | ( | 46.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor3
[factor] |
1. 0
2. 200
3. 210
4. 220
5. 230
6. 300
7. 320
8. 330
9. 340
10. 350
11. 400
12. 402
13. 404
14. 406
15. 415
16. 420
17. 430
18. 480
19. 482
20. 483
21. 485
22. 490
23. 495
24. 500
25. 950
26. 960
27. 970
28. 980
29. 990 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 3.1% | ) | | 1 | ( | 0.3% | ) | | 87 | ( | 30.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 13 | ( | 4.5% | ) | | 10 | ( | 3.4% | ) | | 9 | ( | 3.1% | ) | | 5 | ( | 1.7% | ) | | 9 | ( | 3.1% | ) | | 3 | ( | 1.0% | ) | | 3 | ( | 1.0% | ) | | 19 | ( | 6.6% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 2.1% | ) | | 104 | ( | 35.9% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor3
[factor] |
1. 0
2. 200
3. 210
4. 220
5. 230
6. 300
7. 320
8. 330
9. 340
10. 350
11. 400
12. 402
13. 404
14. 406
15. 415
16. 420
17. 430
18. 480
19. 482
20. 483
21. 485
22. 490
23. 495
24. 500
25. 950
26. 960
27. 970
28. 980
29. 990 |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 19 | ( | 1.8% | ) | | 5 | ( | 0.5% | ) | | 235 | ( | 21.7% | ) | | 35 | ( | 3.2% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 1.0% | ) | | 11 | ( | 1.0% | ) | | 16 | ( | 1.5% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 0.8% | ) | | 10 | ( | 0.9% | ) | | 8 | ( | 0.7% | ) | | 44 | ( | 4.1% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 15 | ( | 1.4% | ) | | 646 | ( | 59.5% | ) | | 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor3
[factor] |
1. 0
2. 200
3. 210
4. 220
5. 230
6. 300
7. 320
8. 330
9. 340
10. 350
11. 400
12. 402
13. 404
14. 406
15. 415
16. 420
17. 430
18. 480
19. 482
20. 483
21. 485
22. 490
23. 495
24. 500
25. 950
26. 960
27. 970
28. 980
29. 990 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 62.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 4
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 4 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2910
All data
st_css() #IMPORTANT!
cssitespecificfactor4 <- as.factor(trimws(d[,"cssitespecificfactor4"]))
new.d <- data.frame(new.d, cssitespecificfactor4)
new.d <- apply_labels(new.d, cssitespecificfactor4 = "cs_site_specific_factor4")
temp.d <- data.frame (new.d.1, cssitespecificfactor4)
summarytools::view(dfSummary(new.d$cssitespecificfactor4, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor4
[labelled, factor] |
cs_site_specific_factor4 |
1. 110
2. 120
3. 140
4. 150
5. 210
6. 220
7. 230
8. 240
9. 250
10. 310
11. 330
12. 350
13. 430
14. 440
15. 450
16. 510
17. 520
18. 540
19. 550
20. 988 |
| 19 | ( | 0.9% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 38 | ( | 1.8% | ) | | 9 | ( | 0.4% | ) | | 10 | ( | 0.5% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 73 | ( | 3.4% | ) | | 1 | ( | 0.0% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 3 | ( | 0.1% | ) | | 11 | ( | 0.5% | ) | | 1 | ( | 0.0% | ) | | 2 | ( | 0.1% | ) | | 1 | ( | 0.0% | ) | | 278 | ( | 13.0% | ) | | 1675 | ( | 78.6% | ) |
|
 |
35
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor4
[factor] |
1. 110
2. 120
3. 140
4. 150
5. 210
6. 220
7. 230
8. 240
9. 250
10. 310
11. 330
12. 350
13. 430
14. 440
15. 450
16. 510
17. 520
18. 540
19. 550
20. 988 |
| 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.6% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 4.8% | ) | | 167 | ( | 88.4% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor4
[factor] |
1. 110
2. 120
3. 140
4. 150
5. 210
6. 220
7. 230
8. 240
9. 250
10. 310
11. 330
12. 350
13. 430
14. 440
15. 450
16. 510
17. 520
18. 540
19. 550
20. 988 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 9 | ( | 5.2% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 5.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 4.6% | ) | | 145 | ( | 83.3% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor4
[factor] |
1. 110
2. 120
3. 140
4. 150
5. 210
6. 220
7. 230
8. 240
9. 250
10. 310
11. 330
12. 350
13. 430
14. 440
15. 450
16. 510
17. 520
18. 540
19. 550
20. 988 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.8% | ) | | 3 | ( | 1.3% | ) | | 2 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 4 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 21 | ( | 8.9% | ) | | 201 | ( | 84.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor4
[factor] |
1. 110
2. 120
3. 140
4. 150
5. 210
6. 220
7. 230
8. 240
9. 250
10. 310
11. 330
12. 350
13. 430
14. 440
15. 450
16. 510
17. 520
18. 540
19. 550
20. 988 |
| 7 | ( | 4.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 19 | ( | 13.4% | ) | | 113 | ( | 79.6% | ) |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor4
[factor] |
1. 110
2. 120
3. 140
4. 150
5. 210
6. 220
7. 230
8. 240
9. 250
10. 310
11. 330
12. 350
13. 430
14. 440
15. 450
16. 510
17. 520
18. 540
19. 550
20. 988 |
| 2 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 4 | ( | 1.4% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 7.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 23 | ( | 7.9% | ) | | 231 | ( | 79.7% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor4
[factor] |
1. 110
2. 120
3. 140
4. 150
5. 210
6. 220
7. 230
8. 240
9. 250
10. 310
11. 330
12. 350
13. 430
14. 440
15. 450
16. 510
17. 520
18. 540
19. 550
20. 988 |
| 8 | ( | 0.7% | ) | | 1 | ( | 0.1% | ) | | 0 | ( | 0.0% | ) | | 21 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 32 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 9 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.1% | ) | | 197 | ( | 18.2% | ) | | 803 | ( | 74.1% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor4
[factor] |
1. 110
2. 120
3. 140
4. 150
5. 210
6. 220
7. 230
8. 240
9. 250
10. 310
11. 330
12. 350
13. 430
14. 440
15. 450
16. 510
17. 520
18. 540
19. 550
20. 988 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 15 | ( | 93.8% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 5
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 5 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2920
All data
st_css() #IMPORTANT!
cssitespecificfactor5 <- as.factor(trimws(d[,"cssitespecificfactor5"]))
new.d <- data.frame(new.d, cssitespecificfactor5)
new.d <- apply_labels(new.d, cssitespecificfactor5 = "cs_site_specific_factor5")
temp.d <- data.frame (new.d.1, cssitespecificfactor5)
summarytools::view(dfSummary(new.d$cssitespecificfactor5, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor5
[labelled, factor] |
cs_site_specific_factor5 |
1. 988 |
|
 |
34
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor5
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor5
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor5
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor5
[factor] |
1. 988 |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor5
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor5
[factor] |
1. 988 |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor5
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
CS SITE-SPECIFIC FACTOR 6
Description: Identifies additional information needed to generate stage, or prognostic factors that have an effect on stage or survival.
Rationale: Site-specific factors are used to record additional staging information needed by Collaborative Staging to derive TNM and/or SEER Summary Stage codes for particular site-histology schema.
Codes (The information recorded in CS Site-Specific Factor 6 differs for each anatomic site. See the most current version of the Collaborative Stage Data Collection System (http://cancerstaging.org),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2930
All data
st_css() #IMPORTANT!
cssitespecificfactor6 <- as.factor(trimws(d[,"cssitespecificfactor6"]))
new.d <- data.frame(new.d, cssitespecificfactor6)
new.d <- apply_labels(new.d, cssitespecificfactor6 = "cs_site_specific_factor6")
temp.d <- data.frame (new.d.1, cssitespecificfactor6)
summarytools::view(dfSummary(new.d$cssitespecificfactor6, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cssitespecificfactor6
[labelled, factor] |
cs_site_specific_factor6 |
1. 988 |
|
 |
34
(1.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor6
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor6
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor6
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor6
[factor] |
1. 988 |
|
 |
32
(18.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor6
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor6
[factor] |
1. 988 |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
cs_site_specific_factor6
[factor] |
1. 988 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED AJCC-6 T
Description: This data item belongs to the Collaborative Stage (CS) Data Collection System which is based on the AJCC Cancer Staging Manual, 6th and 7th editions. AJCC T, N, M plus descriptors and AJCC staging components are composed of combinations of characters, numbers, and/or special characters and can be of varying lengths. To more easily handle these components a numeric code was assigned to each unique category for each T, N, M plus descriptors and AJCC stage for 6th and 7th editions. This field contains the numeric representation for the AJCC 6th edition “T” and is derived from CS coded fields using the CS algorithm. This numeric representation is referred to as the “storage” code and its associated label is referred to as the “display” code. Explanations of the “storage” codes and their corresponding “display” codes can be found in the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx).13 The display code should be used for display on the screen and in reports.
Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.
Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2940
All data
st_css() #IMPORTANT!
derivedajcc6t <- as.factor(trimws(d[,"derivedajcc6t"]))
new.d <- data.frame(new.d, derivedajcc6t)
new.d <- apply_labels(new.d, derivedajcc6t = "derived_ajcc_6t")
temp.d <- data.frame (new.d.1, derivedajcc6t)
summarytools::view(dfSummary(new.d$derivedajcc6t, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedajcc6t
[labelled, factor] |
derived_ajcc_6t |
1. 12
2. 15
3. 18
4. 19
5. 21
6. 22
7. 23
8. 29
9. 31
10. 32
11. 39
12. 40
13. 99 |
| 5 | ( | 0.3% | ) | | 7 | ( | 0.4% | ) | | 608 | ( | 38.3% | ) | | 2 | ( | 0.1% | ) | | 64 | ( | 4.0% | ) | | 25 | ( | 1.6% | ) | | 440 | ( | 27.7% | ) | | 199 | ( | 12.5% | ) | | 99 | ( | 6.2% | ) | | 80 | ( | 5.0% | ) | | 10 | ( | 0.6% | ) | | 30 | ( | 1.9% | ) | | 20 | ( | 1.3% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6t
[factor] |
1. 12
2. 15
3. 18
4. 19
5. 21
6. 22
7. 23
8. 29
9. 31
10. 32
11. 39
12. 40
13. 99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 20 | ( | 22.2% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 5.6% | ) | | 5 | ( | 5.6% | ) | | 38 | ( | 42.2% | ) | | 1 | ( | 1.1% | ) | | 13 | ( | 14.4% | ) | | 6 | ( | 6.7% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6t
[factor] |
1. 12
2. 15
3. 18
4. 19
5. 21
6. 22
7. 23
8. 29
9. 31
10. 32
11. 39
12. 40
13. 99 |
| 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 45 | ( | 52.9% | ) | | 1 | ( | 1.2% | ) | | 8 | ( | 9.4% | ) | | 3 | ( | 3.5% | ) | | 11 | ( | 12.9% | ) | | 7 | ( | 8.2% | ) | | 7 | ( | 8.2% | ) | | 1 | ( | 1.2% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6t
[factor] |
1. 12
2. 15
3. 18
4. 19
5. 21
6. 22
7. 23
8. 29
9. 31
10. 32
11. 39
12. 40
13. 99 |
| 2 | ( | 1.4% | ) | | 1 | ( | 0.7% | ) | | 48 | ( | 34.5% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 5.0% | ) | | 2 | ( | 1.4% | ) | | 47 | ( | 33.8% | ) | | 8 | ( | 5.8% | ) | | 12 | ( | 8.6% | ) | | 9 | ( | 6.5% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) | | 1 | ( | 0.7% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6t
[factor] |
1. 12
2. 15
3. 18
4. 19
5. 21
6. 22
7. 23
8. 29
9. 31
10. 32
11. 39
12. 40
13. 99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 16.9% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.4% | ) | | 1 | ( | 1.7% | ) | | 28 | ( | 47.5% | ) | | 6 | ( | 10.2% | ) | | 6 | ( | 10.2% | ) | | 4 | ( | 6.8% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6t
[factor] |
1. 12
2. 15
3. 18
4. 19
5. 21
6. 22
7. 23
8. 29
9. 31
10. 32
11. 39
12. 40
13. 99 |
| 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 27 | ( | 23.5% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 6.1% | ) | | 2 | ( | 1.7% | ) | | 46 | ( | 40.0% | ) | | 12 | ( | 10.4% | ) | | 10 | ( | 8.7% | ) | | 5 | ( | 4.3% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 3.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6t
[factor] |
1. 12
2. 15
3. 18
4. 19
5. 21
6. 22
7. 23
8. 29
9. 31
10. 32
11. 39
12. 40
13. 99 |
| 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 453 | ( | 41.8% | ) | | 1 | ( | 0.1% | ) | | 35 | ( | 3.2% | ) | | 12 | ( | 1.1% | ) | | 265 | ( | 24.4% | ) | | 160 | ( | 14.7% | ) | | 51 | ( | 4.7% | ) | | 55 | ( | 5.1% | ) | | 7 | ( | 0.6% | ) | | 21 | ( | 1.9% | ) | | 19 | ( | 1.8% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6t
[factor] |
1. 12
2. 15
3. 18
4. 19
5. 21
6. 22
7. 23
8. 29
9. 31
10. 32
11. 39
12. 40
13. 99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 31.2% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED AJCC-6 N
Description: This data item belongs to the Collaborative Stage (CS) Data Collection System which is based on the AJCC Cancer Staging Manual, 6th and 7th editions. AJCC T, N, M plus descriptors and AJCC staging components are composed of combinations of characters, numbers, and/or special characters and can be of varying lengths. To more easily handle these components a numeric code was assigned to each unique category for each T, N, M plus descriptors and AJCC stage for 6th and 7th editions. This field contains the numeric representation for AJCC 6th edition “N” and is derived from CS coded fields using the CS algorithm. This numeric representation is referred to as the “storage” code and its associated label is referred to as the “display” code. Explanations of the “storage” codes and their corresponding “display” codes can be found in the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx).13 The display code should be used for display on the screen and in reports.
Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.
Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2960
All data
st_css() #IMPORTANT!
derivedajcc6n <- as.factor(trimws(d[,"derivedajcc6n"]))
new.d <- data.frame(new.d, derivedajcc6n)
new.d <- apply_labels(new.d, derivedajcc6n = "derived_ajcc_6n")
temp.d <- data.frame (new.d.1, derivedajcc6n)
summarytools::view(dfSummary(new.d$derivedajcc6n, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedajcc6n
[labelled, factor] |
derived_ajcc_6n |
1. 0
2. 10
3. 99 |
| 1496 | ( | 94.1% | ) | | 65 | ( | 4.1% | ) | | 28 | ( | 1.8% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6n
[factor] |
1. 0
2. 10
3. 99 |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6n
[factor] |
1. 0
2. 10
3. 99 |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6n
[factor] |
1. 0
2. 10
3. 99 |
| 125 | ( | 89.9% | ) | | 11 | ( | 7.9% | ) | | 3 | ( | 2.2% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6n
[factor] |
1. 0
2. 10
3. 99 |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6n
[factor] |
1. 0
2. 10
3. 99 |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6n
[factor] |
1. 0
2. 10
3. 99 |
| 1023 | ( | 94.3% | ) | | 38 | ( | 3.5% | ) | | 24 | ( | 2.2% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6n
[factor] |
1. 0
2. 10
3. 99 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED AJCC-6 M
Description: This data item belongs to the Collaborative Stage (CS) Data Collection System which is based on the AJCC Cancer Staging Manual, 6th and 7th editions. AJCC T, N, M plus descriptors and AJCC staging components are composed of combinations of characters, numbers, and/or special characters and can be of varying lengths. To more easily handle these components a numeric code was assigned to each unique category for each T, N, M plus descriptors and AJCC stage for 6th and 7th editions. This field contains the numeric representation for AJCC 6th edition “M” and is derived from CS coded fields using the CS algorithm. This numeric representation is referred to as the “storage” code and its associated label is referred to as the “display” code. Explanations of the “storage” codes and their corresponding “display” codes can be found in the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx).13 The display code should be used for display on the screen and in reports.
Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.
Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#2980
All data
st_css() #IMPORTANT!
derivedajcc6m <- as.factor(trimws(d[,"derivedajcc6m"]))
new.d <- data.frame(new.d, derivedajcc6m)
new.d <- apply_labels(new.d, derivedajcc6m = "derived_ajcc_6m")
temp.d <- data.frame (new.d.1, derivedajcc6m)
summarytools::view(dfSummary(new.d$derivedajcc6m, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedajcc6m
[labelled, factor] |
derived_ajcc_6m |
1. 0
2. 11
3. 12
4. 13
5. 19
6. 99 |
| 1531 | ( | 96.3% | ) | | 7 | ( | 0.4% | ) | | 30 | ( | 1.9% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 19 | ( | 1.2% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6m
[factor] |
1. 0
2. 11
3. 12
4. 13
5. 19
6. 99 |
| 88 | ( | 97.8% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6m
[factor] |
1. 0
2. 11
3. 12
4. 13
5. 19
6. 99 |
| 83 | ( | 97.6% | ) | | 1 | ( | 1.2% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6m
[factor] |
1. 0
2. 11
3. 12
4. 13
5. 19
6. 99 |
| 134 | ( | 96.4% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6m
[factor] |
1. 0
2. 11
3. 12
4. 13
5. 19
6. 99 |
| 56 | ( | 94.9% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 5.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6m
[factor] |
1. 0
2. 11
3. 12
4. 13
5. 19
6. 99 |
| 113 | ( | 98.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6m
[factor] |
1. 0
2. 11
3. 12
4. 13
5. 19
6. 99 |
| 1041 | ( | 95.9% | ) | | 5 | ( | 0.5% | ) | | 20 | ( | 1.8% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 17 | ( | 1.6% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6m
[factor] |
1. 0
2. 11
3. 12
4. 13
5. 19
6. 99 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED AJCC-6 STAGE GRP
Description: This data item belongs to the Collaborative Stage (CS) Data Collection System which is based on the AJCC Cancer Staging Manual, 6th and 7th editions. AJCC T, N, M plus descriptors and AJCC staging components are composed of combinations of characters, numbers, and/or special characters and can be of varying lengths. To more easily handle these components a numeric code was assigned to each unique category for each T, N, M plus descriptors and AJCC stage for 6th and 7th editions. This field contains the numeric representation for the AJCC 6th edition “Stage Group” and is derived from CS coded fields using the CS algorithm. This numeric representation is referred to as the “storage” code and its associated label is referred to as the “display” code. Explanations of the “storage” codes and their corresponding “display” codes can be found in the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx).13 The display code should be used for display on the screen and in reports.
Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.
Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3000
All data
st_css() #IMPORTANT!
derivedajcc6stagegrp <- as.factor(trimws(d[,"derivedajcc6stagegrp"]))
new.d <- data.frame(new.d, derivedajcc6stagegrp)
new.d <- apply_labels(new.d, derivedajcc6stagegrp = "derived_ajcc_6_stage_grp")
temp.d <- data.frame (new.d.1, derivedajcc6stagegrp)
summarytools::view(dfSummary(new.d$derivedajcc6stagegrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedajcc6stagegrp
[labelled, factor] |
derived_ajcc_6_stage_grp |
1. 10
2. 30
3. 50
4. 70
5. 99 |
| 4 | ( | 0.3% | ) | | 1281 | ( | 80.6% | ) | | 155 | ( | 9.8% | ) | | 112 | ( | 7.0% | ) | | 37 | ( | 2.3% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6_stage_grp
[factor] |
1. 10
2. 30
3. 50
4. 70
5. 99 |
| 0 | ( | 0.0% | ) | | 67 | ( | 74.4% | ) | | 16 | ( | 17.8% | ) | | 7 | ( | 7.8% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6_stage_grp
[factor] |
1. 10
2. 30
3. 50
4. 70
5. 99 |
| 0 | ( | 0.0% | ) | | 74 | ( | 87.1% | ) | | 8 | ( | 9.4% | ) | | 3 | ( | 3.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6_stage_grp
[factor] |
1. 10
2. 30
3. 50
4. 70
5. 99 |
| 2 | ( | 1.4% | ) | | 106 | ( | 76.3% | ) | | 16 | ( | 11.5% | ) | | 13 | ( | 9.4% | ) | | 2 | ( | 1.4% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6_stage_grp
[factor] |
1. 10
2. 30
3. 50
4. 70
5. 99 |
| 0 | ( | 0.0% | ) | | 45 | ( | 76.3% | ) | | 9 | ( | 15.3% | ) | | 5 | ( | 8.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6_stage_grp
[factor] |
1. 10
2. 30
3. 50
4. 70
5. 99 |
| 0 | ( | 0.0% | ) | | 91 | ( | 79.1% | ) | | 12 | ( | 10.4% | ) | | 11 | ( | 9.6% | ) | | 1 | ( | 0.9% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6_stage_grp
[factor] |
1. 10
2. 30
3. 50
4. 70
5. 99 |
| 2 | ( | 0.2% | ) | | 883 | ( | 81.4% | ) | | 94 | ( | 8.7% | ) | | 72 | ( | 6.6% | ) | | 34 | ( | 3.1% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_6_stage_grp
[factor] |
1. 10
2. 30
3. 50
4. 70
5. 99 |
| 0 | ( | 0.0% | ) | | 15 | ( | 93.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SS1977
Description: This item is the derived “SEER Summary Stage 1977” from the CS algorithm (or EOD codes) effective with 2004 diagnosis.
Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.
Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3010
All data
st_css() #IMPORTANT!
derivedss1977 <- as.factor(trimws(d[,"derivedss1977"]))
new.d <- data.frame(new.d, derivedss1977)
new.d <- apply_labels(new.d, derivedss1977 = "derived_ss1977")
temp.d <- data.frame (new.d.1, derivedss1977)
summarytools::view(dfSummary(new.d$derivedss1977, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedss1977
[labelled, factor] |
derived_ss1977 |
1. 1
2. 2
3. 3
4. 4
5. 7
6. 9 |
| 1302 | ( | 81.9% | ) | | 175 | ( | 11.0% | ) | | 20 | ( | 1.3% | ) | | 32 | ( | 2.0% | ) | | 40 | ( | 2.5% | ) | | 20 | ( | 1.3% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss1977
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 7
6. 9 |
| 67 | ( | 74.4% | ) | | 16 | ( | 17.8% | ) | | 1 | ( | 1.1% | ) | | 4 | ( | 4.4% | ) | | 2 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss1977
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 7
6. 9 |
| 74 | ( | 87.1% | ) | | 8 | ( | 9.4% | ) | | 1 | ( | 1.2% | ) | | 1 | ( | 1.2% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss1977
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 7
6. 9 |
| 109 | ( | 78.4% | ) | | 16 | ( | 11.5% | ) | | 3 | ( | 2.2% | ) | | 7 | ( | 5.0% | ) | | 3 | ( | 2.2% | ) | | 1 | ( | 0.7% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss1977
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 7
6. 9 |
| 45 | ( | 76.3% | ) | | 9 | ( | 15.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.4% | ) | | 3 | ( | 5.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss1977
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 7
6. 9 |
| 92 | ( | 80.0% | ) | | 15 | ( | 13.0% | ) | | 2 | ( | 1.7% | ) | | 3 | ( | 2.6% | ) | | 3 | ( | 2.6% | ) | | 0 | ( | 0.0% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss1977
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 7
6. 9 |
| 900 | ( | 82.9% | ) | | 110 | ( | 10.1% | ) | | 13 | ( | 1.2% | ) | | 15 | ( | 1.4% | ) | | 28 | ( | 2.6% | ) | | 19 | ( | 1.8% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss1977
[factor] |
1. 1
2. 2
3. 3
4. 4
5. 7
6. 9 |
| 15 | ( | 93.8% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SS2000
Description: This item is the derived “SEER Summary Stage 2000” from the CS algorithm (or EOD codes) effective with 2004 diagnosis.
Rationale: The Collaborative Stage Data Collection System was designed by a joint task force including representatives from SEER, ACoS, CDC, NAACCR, NCRA, CCCR, CPAC, and AJCC, to provide a single uniform set of codes and rules for coding extent of disease (EOD) and stage information to meet the needs of all of the participating standard setters. When CS data items are coded, a computer algorithm provides the derivation of T, N, M, and stage-based on AJCC Cancer Staging Manual 6th & 7th Editions, SEER Summary Stage 1977, and SEER Summary Stage 2000. There are separate derived CS fields in the NAACCR record based on AJCC 6th Edition for 2004+ cases and AJCC 7th Edition for 2010+ cases.
Codes (See the most current version of the Collaborative Stage Data Collection System (https://cancerstaging.org/cstage/Pages/default.aspx),13 for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3020
All data
st_css() #IMPORTANT!
derivedss2000 <- as.factor(d[,"derivedss2000"])
levels(derivedss2000) <- list(In_situ.0="0",
Localized.1="1",
Direct_extension.2="2",
Lymph_nodes_only.3="3",
Extension_nodes.4="4",
Distant.7="7",
Unknown.9="9")
derivedss2000 <- relevel(derivedss2000, ref="Localized.1")
new.d <- data.frame(new.d, derivedss2000)
new.d <- apply_labels(new.d, derivedss2000 = "Tumor Staging")
#summary(new.d$derivedss2000)
temp.d <- data.frame (new.d.1, derivedss2000)
summarytools::view(dfSummary(new.d$derivedss2000, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedss2000
[labelled, factor] |
Tumor Staging |
1. Localized.1
2. In_situ.0
3. Direct_extension.2
4. Lymph_nodes_only.3
5. Extension_nodes.4
6. Distant.7
7. Unknown.9 |
| 1264 | ( | 79.5% | ) | | 0 | ( | 0.0% | ) | | 213 | ( | 13.4% | ) | | 17 | ( | 1.1% | ) | | 34 | ( | 2.1% | ) | | 41 | ( | 2.6% | ) | | 20 | ( | 1.3% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss_2000
[factor] |
1. Localized.1
2. In_situ.0
3. Direct_extension.2
4. Lymph_nodes_only.3
5. Extension_nodes.4
6. Distant.7
7. Unknown.9 |
| 61 | ( | 67.8% | ) | | 0 | ( | 0.0% | ) | | 22 | ( | 24.4% | ) | | 1 | ( | 1.1% | ) | | 4 | ( | 4.4% | ) | | 2 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss_2000
[factor] |
1. Localized.1
2. In_situ.0
3. Direct_extension.2
4. Lymph_nodes_only.3
5. Extension_nodes.4
6. Distant.7
7. Unknown.9 |
| 71 | ( | 83.5% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 12.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 2 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss_2000
[factor] |
1. Localized.1
2. In_situ.0
3. Direct_extension.2
4. Lymph_nodes_only.3
5. Extension_nodes.4
6. Distant.7
7. Unknown.9 |
| 103 | ( | 74.1% | ) | | 0 | ( | 0.0% | ) | | 22 | ( | 15.8% | ) | | 3 | ( | 2.2% | ) | | 7 | ( | 5.0% | ) | | 3 | ( | 2.2% | ) | | 1 | ( | 0.7% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss_2000
[factor] |
1. Localized.1
2. In_situ.0
3. Direct_extension.2
4. Lymph_nodes_only.3
5. Extension_nodes.4
6. Distant.7
7. Unknown.9 |
| 41 | ( | 69.5% | ) | | 0 | ( | 0.0% | ) | | 13 | ( | 22.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.4% | ) | | 3 | ( | 5.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss_2000
[factor] |
1. Localized.1
2. In_situ.0
3. Direct_extension.2
4. Lymph_nodes_only.3
5. Extension_nodes.4
6. Distant.7
7. Unknown.9 |
| 85 | ( | 73.9% | ) | | 0 | ( | 0.0% | ) | | 22 | ( | 19.1% | ) | | 1 | ( | 0.9% | ) | | 4 | ( | 3.5% | ) | | 3 | ( | 2.6% | ) | | 0 | ( | 0.0% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss_2000
[factor] |
1. Localized.1
2. In_situ.0
3. Direct_extension.2
4. Lymph_nodes_only.3
5. Extension_nodes.4
6. Distant.7
7. Unknown.9 |
| 888 | ( | 81.8% | ) | | 0 | ( | 0.0% | ) | | 122 | ( | 11.2% | ) | | 12 | ( | 1.1% | ) | | 16 | ( | 1.5% | ) | | 28 | ( | 2.6% | ) | | 19 | ( | 1.8% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ss_2000
[factor] |
1. Localized.1
2. In_situ.0
3. Direct_extension.2
4. Lymph_nodes_only.3
5. Extension_nodes.4
6. Distant.7
7. Unknown.9 |
| 15 | ( | 93.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 1
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
- 00000 No secondary diagnoses documented
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3110
All data
st_css() #IMPORTANT!
comorbidcomplication1 <- as.factor(d[,"comorbidcomplication1"])
levels(comorbidcomplication1) <- list(No_secondary.0="0")
new.d <- data.frame(new.d, comorbidcomplication1)
new.d <- apply_labels(new.d, comorbidcomplication1 = "comorbid_complication1")
#summary(new.d$comorbidcomplication1)
temp.d <- data.frame (new.d.1, comorbidcomplication1)
summarytools::view(dfSummary(new.d$comorbidcomplication1, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication1
[labelled, factor] |
comorbid_complication1 |
1. No_secondary.0 |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication1
[factor] |
1. No_secondary.0 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication1
[factor] |
1. No_secondary.0 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication1
[factor] |
1. No_secondary.0 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication1
[factor] |
1. No_secondary.0 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication1
[factor] |
1. No_secondary.0 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication1
[factor] |
1. No_secondary.0 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication1
[factor] |
1. No_secondary.0 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 2
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3120
All data
st_css() #IMPORTANT!
comorbidcomplication2 <- as.factor(d[,"comorbidcomplication2"])
new.d <- data.frame(new.d, comorbidcomplication2)
new.d <- apply_labels(new.d, comorbidcomplication2 = "comorbid_complication2")
#summary(new.d$comorbidcomplication2)
temp.d <- data.frame (new.d.1, comorbidcomplication2)
summarytools::view(dfSummary(new.d$comorbidcomplication2, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication2
[labelled, factor] |
comorbid_complication2 |
1. 13644
2. 25000
3. 27200
4. 27240
5. 27400
6. 27490
7. 27800
8. 27801
9. 28590
10. 30510
11. 35180
12. 36590
13. 40100
14. 40110
15. 40190
16. 40493
17. 42400
18. 44422
19. 47790
20. 53081
21. 55090
22. 56010
23. 56410
24. 56800
25. 57150
26. 57390
27. 5780 ·
28. 57810
29. 5849 ·
30. 59410
31. 5989 ·
32. 59900
33. 60000
34. 60001
35. 60290
36. 60784
37. 64800
38. 69010
39. 71690
40. 7213 ·
41. 72660
42. 72950
43. 72981
44. 78020
45. 78863
46. 78900
47. 79093
48. 99990 |
| 1 | ( | 1.1% | ) | | 5 | ( | 5.6% | ) | | 2 | ( | 2.2% | ) | | 5 | ( | 5.6% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 4 | ( | 4.4% | ) | | 1 | ( | 1.1% | ) | | 2 | ( | 2.2% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 21 | ( | 23.3% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 3 | ( | 3.3% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 5 | ( | 5.6% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 2 | ( | 2.2% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 2 | ( | 2.2% | ) | | 2 | ( | 2.2% | ) |
|
 |
2077
(95.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication2
[factor] |
1. 13644
2. 25000
3. 27200
4. 27240
5. 27400
6. 27490
7. 27800
8. 27801
9. 28590
10. 30510
11. 35180
12. 36590
13. 40100
14. 40110
15. 40190
16. 40493
17. 42400
18. 44422
19. 47790
20. 53081
21. 55090
22. 56010
23. 56410
24. 56800
25. 57150
26. 57390
27. 5780 ·
28. 57810
29. 5849 ·
30. 59410
31. 5989 ·
32. 59900
33. 60000
34. 60001
35. 60290
36. 60784
37. 64800
38. 69010
39. 71690
40. 7213 ·
41. 72660
42. 72950
43. 72981
44. 78020
45. 78863
46. 78900
47. 79093
48. 99990 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication2
[factor] |
1. 13644
2. 25000
3. 27200
4. 27240
5. 27400
6. 27490
7. 27800
8. 27801
9. 28590
10. 30510
11. 35180
12. 36590
13. 40100
14. 40110
15. 40190
16. 40493
17. 42400
18. 44422
19. 47790
20. 53081
21. 55090
22. 56010
23. 56410
24. 56800
25. 57150
26. 57390
27. 5780 ·
28. 57810
29. 5849 ·
30. 59410
31. 5989 ·
32. 59900
33. 60000
34. 60001
35. 60290
36. 60784
37. 64800
38. 69010
39. 71690
40. 7213 ·
41. 72660
42. 72950
43. 72981
44. 78020
45. 78863
46. 78900
47. 79093
48. 99990 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication2
[factor] |
1. 13644
2. 25000
3. 27200
4. 27240
5. 27400
6. 27490
7. 27800
8. 27801
9. 28590
10. 30510
11. 35180
12. 36590
13. 40100
14. 40110
15. 40190
16. 40493
17. 42400
18. 44422
19. 47790
20. 53081
21. 55090
22. 56010
23. 56410
24. 56800
25. 57150
26. 57390
27. 5780 ·
28. 57810
29. 5849 ·
30. 59410
31. 5989 ·
32. 59900
33. 60000
34. 60001
35. 60290
36. 60784
37. 64800
38. 69010
39. 71690
40. 7213 ·
41. 72660
42. 72950
43. 72981
44. 78020
45. 78863
46. 78900
47. 79093
48. 99990 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication2
[factor] |
1. 13644
2. 25000
3. 27200
4. 27240
5. 27400
6. 27490
7. 27800
8. 27801
9. 28590
10. 30510
11. 35180
12. 36590
13. 40100
14. 40110
15. 40190
16. 40493
17. 42400
18. 44422
19. 47790
20. 53081
21. 55090
22. 56010
23. 56410
24. 56800
25. 57150
26. 57390
27. 5780 ·
28. 57810
29. 5849 ·
30. 59410
31. 5989 ·
32. 59900
33. 60000
34. 60001
35. 60290
36. 60784
37. 64800
38. 69010
39. 71690
40. 7213 ·
41. 72660
42. 72950
43. 72981
44. 78020
45. 78863
46. 78900
47. 79093
48. 99990 |
| 0 | ( | 0.0% | ) | | 1 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 29.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.9% | ) | | 2 | ( | 11.8% | ) | | 1 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
157
(90.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication2
[factor] |
1. 13644
2. 25000
3. 27200
4. 27240
5. 27400
6. 27490
7. 27800
8. 27801
9. 28590
10. 30510
11. 35180
12. 36590
13. 40100
14. 40110
15. 40190
16. 40493
17. 42400
18. 44422
19. 47790
20. 53081
21. 55090
22. 56010
23. 56410
24. 56800
25. 57150
26. 57390
27. 5780 ·
28. 57810
29. 5849 ·
30. 59410
31. 5989 ·
32. 59900
33. 60000
34. 60001
35. 60290
36. 60784
37. 64800
38. 69010
39. 71690
40. 7213 ·
41. 72660
42. 72950
43. 72981
44. 78020
45. 78863
46. 78900
47. 79093
48. 99990 |
| 1 | ( | 1.5% | ) | | 4 | ( | 5.9% | ) | | 2 | ( | 2.9% | ) | | 5 | ( | 7.4% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 4 | ( | 5.9% | ) | | 1 | ( | 1.5% | ) | | 2 | ( | 2.9% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 14 | ( | 20.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 2 | ( | 2.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 4 | ( | 5.9% | ) | | 1 | ( | 1.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 1 | ( | 1.5% | ) | | 2 | ( | 2.9% | ) | | 2 | ( | 2.9% | ) |
|
 |
222
(76.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication2
[factor] |
1. 13644
2. 25000
3. 27200
4. 27240
5. 27400
6. 27490
7. 27800
8. 27801
9. 28590
10. 30510
11. 35180
12. 36590
13. 40100
14. 40110
15. 40190
16. 40493
17. 42400
18. 44422
19. 47790
20. 53081
21. 55090
22. 56010
23. 56410
24. 56800
25. 57150
26. 57390
27. 5780 ·
28. 57810
29. 5849 ·
30. 59410
31. 5989 ·
32. 59900
33. 60000
34. 60001
35. 60290
36. 60784
37. 64800
38. 69010
39. 71690
40. 7213 ·
41. 72660
42. 72950
43. 72981
44. 78020
45. 78863
46. 78900
47. 79093
48. 99990 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication2
[factor] |
1. 13644
2. 25000
3. 27200
4. 27240
5. 27400
6. 27490
7. 27800
8. 27801
9. 28590
10. 30510
11. 35180
12. 36590
13. 40100
14. 40110
15. 40190
16. 40493
17. 42400
18. 44422
19. 47790
20. 53081
21. 55090
22. 56010
23. 56410
24. 56800
25. 57150
26. 57390
27. 5780 ·
28. 57810
29. 5849 ·
30. 59410
31. 5989 ·
32. 59900
33. 60000
34. 60001
35. 60290
36. 60784
37. 64800
38. 69010
39. 71690
40. 7213 ·
41. 72660
42. 72950
43. 72981
44. 78020
45. 78863
46. 78900
47. 79093
48. 99990 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 40.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
11
(68.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 3
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3130
All data
st_css() #IMPORTANT!
comorbidcomplication3 <- as.factor(d[,"comorbidcomplication3"])
new.d <- data.frame(new.d, comorbidcomplication3)
new.d <- apply_labels(new.d, comorbidcomplication3 = "comorbid_complication3")
#summary(new.d$comorbidcomplication3)
temp.d <- data.frame (new.d.1, comorbidcomplication3)
summarytools::view(dfSummary(new.d$comorbidcomplication3, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication3
[labelled, factor] |
comorbid_complication3 |
1. 24490
2. 25000
3. 27200
4. 27240
5. 27490
6. 2752 ·
7. 27651
8. 27800
9. 30002
10. 30500
11. 30510
12. 32723
13. 35400
14. 40190
15. 40390
16. 41040
17. 41200
18. 41400
19. 41490
20. 4186 ·
21. 42832
22. 43600
23. 49600
24. 53081
25. 58550
26. 60000
27. 60784
28. 68220
29. 69806
30. 700 ·
31. 7051 ·
32. 7070 ·
33. 71590
34. 71689
35. 71690
36. 71696
37. 72240
38. 72291
39. 72950
40. 74685
41. 78057
42. 78843
43. 79029
44. 79093
45. 99990 |
| 1 | ( | 1.7% | ) | | 3 | ( | 5.1% | ) | | 1 | ( | 1.7% | ) | | 3 | ( | 5.1% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 2 | ( | 3.4% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 3 | ( | 5.1% | ) | | 2 | ( | 3.4% | ) | | 1 | ( | 1.7% | ) | | 2 | ( | 3.4% | ) | | 2 | ( | 3.4% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 2 | ( | 3.4% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 2 | ( | 3.4% | ) | | 2 | ( | 3.4% | ) | | 1 | ( | 1.7% | ) | | 2 | ( | 3.4% | ) | | 1 | ( | 1.7% | ) |
|
 |
2108
(97.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication3
[factor] |
1. 24490
2. 25000
3. 27200
4. 27240
5. 27490
6. 2752 ·
7. 27651
8. 27800
9. 30002
10. 30500
11. 30510
12. 32723
13. 35400
14. 40190
15. 40390
16. 41040
17. 41200
18. 41400
19. 41490
20. 4186 ·
21. 42832
22. 43600
23. 49600
24. 53081
25. 58550
26. 60000
27. 60784
28. 68220
29. 69806
30. 700 ·
31. 7051 ·
32. 7070 ·
33. 71590
34. 71689
35. 71690
36. 71696
37. 72240
38. 72291
39. 72950
40. 74685
41. 78057
42. 78843
43. 79029
44. 79093
45. 99990 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication3
[factor] |
1. 24490
2. 25000
3. 27200
4. 27240
5. 27490
6. 2752 ·
7. 27651
8. 27800
9. 30002
10. 30500
11. 30510
12. 32723
13. 35400
14. 40190
15. 40390
16. 41040
17. 41200
18. 41400
19. 41490
20. 4186 ·
21. 42832
22. 43600
23. 49600
24. 53081
25. 58550
26. 60000
27. 60784
28. 68220
29. 69806
30. 700 ·
31. 7051 ·
32. 7070 ·
33. 71590
34. 71689
35. 71690
36. 71696
37. 72240
38. 72291
39. 72950
40. 74685
41. 78057
42. 78843
43. 79029
44. 79093
45. 99990 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication3
[factor] |
1. 24490
2. 25000
3. 27200
4. 27240
5. 27490
6. 2752 ·
7. 27651
8. 27800
9. 30002
10. 30500
11. 30510
12. 32723
13. 35400
14. 40190
15. 40390
16. 41040
17. 41200
18. 41400
19. 41490
20. 4186 ·
21. 42832
22. 43600
23. 49600
24. 53081
25. 58550
26. 60000
27. 60784
28. 68220
29. 69806
30. 700 ·
31. 7051 ·
32. 7070 ·
33. 71590
34. 71689
35. 71690
36. 71696
37. 72240
38. 72291
39. 72950
40. 74685
41. 78057
42. 78843
43. 79029
44. 79093
45. 99990 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication3
[factor] |
1. 24490
2. 25000
3. 27200
4. 27240
5. 27490
6. 2752 ·
7. 27651
8. 27800
9. 30002
10. 30500
11. 30510
12. 32723
13. 35400
14. 40190
15. 40390
16. 41040
17. 41200
18. 41400
19. 41490
20. 4186 ·
21. 42832
22. 43600
23. 49600
24. 53081
25. 58550
26. 60000
27. 60784
28. 68220
29. 69806
30. 700 ·
31. 7051 ·
32. 7070 ·
33. 71590
34. 71689
35. 71690
36. 71696
37. 72240
38. 72291
39. 72950
40. 74685
41. 78057
42. 78843
43. 79029
44. 79093
45. 99990 |
| 0 | ( | 0.0% | ) | | 2 | ( | 15.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
161
(92.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication3
[factor] |
1. 24490
2. 25000
3. 27200
4. 27240
5. 27490
6. 2752 ·
7. 27651
8. 27800
9. 30002
10. 30500
11. 30510
12. 32723
13. 35400
14. 40190
15. 40390
16. 41040
17. 41200
18. 41400
19. 41490
20. 4186 ·
21. 42832
22. 43600
23. 49600
24. 53081
25. 58550
26. 60000
27. 60784
28. 68220
29. 69806
30. 700 ·
31. 7051 ·
32. 7070 ·
33. 71590
34. 71689
35. 71690
36. 71696
37. 72240
38. 72291
39. 72950
40. 74685
41. 78057
42. 78843
43. 79029
44. 79093
45. 99990 |
| 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 3 | ( | 7.1% | ) | | 1 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 4.8% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.4% | ) | | 2 | ( | 4.8% | ) | | 2 | ( | 4.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) | | 2 | ( | 4.8% | ) | | 2 | ( | 4.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.4% | ) | | 1 | ( | 2.4% | ) |
|
 |
248
(85.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication3
[factor] |
1. 24490
2. 25000
3. 27200
4. 27240
5. 27490
6. 2752 ·
7. 27651
8. 27800
9. 30002
10. 30500
11. 30510
12. 32723
13. 35400
14. 40190
15. 40390
16. 41040
17. 41200
18. 41400
19. 41490
20. 4186 ·
21. 42832
22. 43600
23. 49600
24. 53081
25. 58550
26. 60000
27. 60784
28. 68220
29. 69806
30. 700 ·
31. 7051 ·
32. 7070 ·
33. 71590
34. 71689
35. 71690
36. 71696
37. 72240
38. 72291
39. 72950
40. 74685
41. 78057
42. 78843
43. 79029
44. 79093
45. 99990 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication3
[factor] |
1. 24490
2. 25000
3. 27200
4. 27240
5. 27490
6. 2752 ·
7. 27651
8. 27800
9. 30002
10. 30500
11. 30510
12. 32723
13. 35400
14. 40190
15. 40390
16. 41040
17. 41200
18. 41400
19. 41490
20. 4186 ·
21. 42832
22. 43600
23. 49600
24. 53081
25. 58550
26. 60000
27. 60784
28. 68220
29. 69806
30. 700 ·
31. 7051 ·
32. 7070 ·
33. 71590
34. 71689
35. 71690
36. 71696
37. 72240
38. 72291
39. 72950
40. 74685
41. 78057
42. 78843
43. 79029
44. 79093
45. 99990 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
12
(75.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 4
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3140
All data
st_css() #IMPORTANT!
comorbidcomplication4 <- as.factor(d[,"comorbidcomplication4"])
new.d <- data.frame(new.d, comorbidcomplication4)
new.d <- apply_labels(new.d, comorbidcomplication4 = "comorbid_complication4")
#summary(new.d$comorbidcomplication4)
temp.d <- data.frame (new.d.1, comorbidcomplication4)
summarytools::view(dfSummary(new.d$comorbidcomplication4, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication4
[labelled, factor] |
comorbid_complication4 |
1. 25000
2. 27240
3. 27490
4. 27610
5. 27800
6. 27802
7. 28249
8. 28860
9. 28981
10. 30510
11. 36501
12. 37862
13. 40190
14. 41491
15. 4280 ·
16. 42890
17. 53030
18. 56210
19. 58490
20. 58881
21. 5920 ·
22. 59200
23. 60000
24. 60001
25. 60784
26. 72450
27. 73100
28. 78060
29. 78062
30. 78841
31. 79093
32. 99990 |
| 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 2 | ( | 5.3% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 3 | ( | 7.9% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 2 | ( | 5.3% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 2 | ( | 5.3% | ) | | 1 | ( | 2.6% | ) | | 2 | ( | 5.3% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) | | 1 | ( | 2.6% | ) |
|
 |
2129
(98.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication4
[factor] |
1. 25000
2. 27240
3. 27490
4. 27610
5. 27800
6. 27802
7. 28249
8. 28860
9. 28981
10. 30510
11. 36501
12. 37862
13. 40190
14. 41491
15. 4280 ·
16. 42890
17. 53030
18. 56210
19. 58490
20. 58881
21. 5920 ·
22. 59200
23. 60000
24. 60001
25. 60784
26. 72450
27. 73100
28. 78060
29. 78062
30. 78841
31. 79093
32. 99990 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication4
[factor] |
1. 25000
2. 27240
3. 27490
4. 27610
5. 27800
6. 27802
7. 28249
8. 28860
9. 28981
10. 30510
11. 36501
12. 37862
13. 40190
14. 41491
15. 4280 ·
16. 42890
17. 53030
18. 56210
19. 58490
20. 58881
21. 5920 ·
22. 59200
23. 60000
24. 60001
25. 60784
26. 72450
27. 73100
28. 78060
29. 78062
30. 78841
31. 79093
32. 99990 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication4
[factor] |
1. 25000
2. 27240
3. 27490
4. 27610
5. 27800
6. 27802
7. 28249
8. 28860
9. 28981
10. 30510
11. 36501
12. 37862
13. 40190
14. 41491
15. 4280 ·
16. 42890
17. 53030
18. 56210
19. 58490
20. 58881
21. 5920 ·
22. 59200
23. 60000
24. 60001
25. 60784
26. 72450
27. 73100
28. 78060
29. 78062
30. 78841
31. 79093
32. 99990 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication4
[factor] |
1. 25000
2. 27240
3. 27490
4. 27610
5. 27800
6. 27802
7. 28249
8. 28860
9. 28981
10. 30510
11. 36501
12. 37862
13. 40190
14. 41491
15. 4280 ·
16. 42890
17. 53030
18. 56210
19. 58490
20. 58881
21. 5920 ·
22. 59200
23. 60000
24. 60001
25. 60784
26. 72450
27. 73100
28. 78060
29. 78062
30. 78841
31. 79093
32. 99990 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 22.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
165
(94.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication4
[factor] |
1. 25000
2. 27240
3. 27490
4. 27610
5. 27800
6. 27802
7. 28249
8. 28860
9. 28981
10. 30510
11. 36501
12. 37862
13. 40190
14. 41491
15. 4280 ·
16. 42890
17. 53030
18. 56210
19. 58490
20. 58881
21. 5920 ·
22. 59200
23. 60000
24. 60001
25. 60784
26. 72450
27. 73100
28. 78060
29. 78062
30. 78841
31. 79093
32. 99990 |
| 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 8.0% | ) | | 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 4.0% | ) | | 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 4.0% | ) | | 1 | ( | 4.0% | ) | | 1 | ( | 4.0% | ) | | 1 | ( | 4.0% | ) | | 2 | ( | 8.0% | ) | | 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 4.0% | ) | | 2 | ( | 8.0% | ) | | 1 | ( | 4.0% | ) | | 2 | ( | 8.0% | ) | | 1 | ( | 4.0% | ) | | 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 4.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 4.0% | ) |
|
 |
265
(91.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication4
[factor] |
1. 25000
2. 27240
3. 27490
4. 27610
5. 27800
6. 27802
7. 28249
8. 28860
9. 28981
10. 30510
11. 36501
12. 37862
13. 40190
14. 41491
15. 4280 ·
16. 42890
17. 53030
18. 56210
19. 58490
20. 58881
21. 5920 ·
22. 59200
23. 60000
24. 60001
25. 60784
26. 72450
27. 73100
28. 78060
29. 78062
30. 78841
31. 79093
32. 99990 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication4
[factor] |
1. 25000
2. 27240
3. 27490
4. 27610
5. 27800
6. 27802
7. 28249
8. 28860
9. 28981
10. 30510
11. 36501
12. 37862
13. 40190
14. 41491
15. 4280 ·
16. 42890
17. 53030
18. 56210
19. 58490
20. 58881
21. 5920 ·
22. 59200
23. 60000
24. 60001
25. 60784
26. 72450
27. 73100
28. 78060
29. 78062
30. 78841
31. 79093
32. 99990 |
| 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
12
(75.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 5
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3150
All data
st_css() #IMPORTANT!
comorbidcomplication5 <- as.factor(d[,"comorbidcomplication5"])
new.d <- data.frame(new.d, comorbidcomplication5)
new.d <- apply_labels(new.d, comorbidcomplication5 = "comorbid_complication5")
#summary(new.d$comorbidcomplication5)
temp.d <- data.frame (new.d.1, comorbidcomplication5)
summarytools::view(dfSummary(new.d$comorbidcomplication5, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication5
[labelled, factor] |
comorbid_complication5 |
1. 13500
2. 25000
3. 27240
4. 27803
5. 29600
6. 29630
7. 30510
8. 35400
9. 36800
10. 37515
11. 40190
12. 43820
13. 45340
14. 59100
15. 5932 ·
16. 59971
17. 60784
18. 70000
19. 7032 ·
20. 78060
21. 78590
22. 78701
23. 78820
24. 78862
25. 99990 |
| 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 4 | ( | 14.3% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) |
|
 |
2139
(98.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication5
[factor] |
1. 13500
2. 25000
3. 27240
4. 27803
5. 29600
6. 29630
7. 30510
8. 35400
9. 36800
10. 37515
11. 40190
12. 43820
13. 45340
14. 59100
15. 5932 ·
16. 59971
17. 60784
18. 70000
19. 7032 ·
20. 78060
21. 78590
22. 78701
23. 78820
24. 78862
25. 99990 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication5
[factor] |
1. 13500
2. 25000
3. 27240
4. 27803
5. 29600
6. 29630
7. 30510
8. 35400
9. 36800
10. 37515
11. 40190
12. 43820
13. 45340
14. 59100
15. 5932 ·
16. 59971
17. 60784
18. 70000
19. 7032 ·
20. 78060
21. 78590
22. 78701
23. 78820
24. 78862
25. 99990 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication5
[factor] |
1. 13500
2. 25000
3. 27240
4. 27803
5. 29600
6. 29630
7. 30510
8. 35400
9. 36800
10. 37515
11. 40190
12. 43820
13. 45340
14. 59100
15. 5932 ·
16. 59971
17. 60784
18. 70000
19. 7032 ·
20. 78060
21. 78590
22. 78701
23. 78820
24. 78862
25. 99990 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication5
[factor] |
1. 13500
2. 25000
3. 27240
4. 27803
5. 29600
6. 29630
7. 30510
8. 35400
9. 36800
10. 37515
11. 40190
12. 43820
13. 45340
14. 59100
15. 5932 ·
16. 59971
17. 60784
18. 70000
19. 7032 ·
20. 78060
21. 78590
22. 78701
23. 78820
24. 78862
25. 99990 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
169
(97.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication5
[factor] |
1. 13500
2. 25000
3. 27240
4. 27803
5. 29600
6. 29630
7. 30510
8. 35400
9. 36800
10. 37515
11. 40190
12. 43820
13. 45340
14. 59100
15. 5932 ·
16. 59971
17. 60784
18. 70000
19. 7032 ·
20. 78060
21. 78590
22. 78701
23. 78820
24. 78862
25. 99990 |
| 1 | ( | 5.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) | | 3 | ( | 15.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.0% | ) | | 1 | ( | 5.0% | ) |
|
 |
270
(93.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication5
[factor] |
1. 13500
2. 25000
3. 27240
4. 27803
5. 29600
6. 29630
7. 30510
8. 35400
9. 36800
10. 37515
11. 40190
12. 43820
13. 45340
14. 59100
15. 5932 ·
16. 59971
17. 60784
18. 70000
19. 7032 ·
20. 78060
21. 78590
22. 78701
23. 78820
24. 78862
25. 99990 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication5
[factor] |
1. 13500
2. 25000
3. 27240
4. 27803
5. 29600
6. 29630
7. 30510
8. 35400
9. 36800
10. 37515
11. 40190
12. 43820
13. 45340
14. 59100
15. 5932 ·
16. 59971
17. 60784
18. 70000
19. 7032 ·
20. 78060
21. 78590
22. 78701
23. 78820
24. 78862
25. 99990 |
| 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
13
(81.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 6
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3160
All data
st_css() #IMPORTANT!
comorbidcomplication6 <- as.factor(d[,"comorbidcomplication6"])
new.d <- data.frame(new.d, comorbidcomplication6)
new.d <- apply_labels(new.d, comorbidcomplication6 = "comorbid_complication6")
#summary(new.d$comorbidcomplication6)
temp.d <- data.frame (new.d.1, comorbidcomplication6)
summarytools::view(dfSummary(new.d$comorbidcomplication6, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication6
[labelled, factor] |
comorbid_complication6 |
1. 27240
2. 30401
3. 35180
4. 40190
5. 41400
6. 41519
7. 43812
8. 4720 ·
9. 5739 ·
10. 59390
11. 60010
12. 60784
13. 72420
14. 73390
15. 78659
16. 78865
17. 79029
18. 79093 |
| 2 | ( | 9.5% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 2 | ( | 9.5% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 2 | ( | 9.5% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) | | 1 | ( | 4.8% | ) |
|
 |
2146
(99.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication6
[factor] |
1. 27240
2. 30401
3. 35180
4. 40190
5. 41400
6. 41519
7. 43812
8. 4720 ·
9. 5739 ·
10. 59390
11. 60010
12. 60784
13. 72420
14. 73390
15. 78659
16. 78865
17. 79029
18. 79093 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication6
[factor] |
1. 27240
2. 30401
3. 35180
4. 40190
5. 41400
6. 41519
7. 43812
8. 4720 ·
9. 5739 ·
10. 59390
11. 60010
12. 60784
13. 72420
14. 73390
15. 78659
16. 78865
17. 79029
18. 79093 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication6
[factor] |
1. 27240
2. 30401
3. 35180
4. 40190
5. 41400
6. 41519
7. 43812
8. 4720 ·
9. 5739 ·
10. 59390
11. 60010
12. 60784
13. 72420
14. 73390
15. 78659
16. 78865
17. 79029
18. 79093 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication6
[factor] |
1. 27240
2. 30401
3. 35180
4. 40190
5. 41400
6. 41519
7. 43812
8. 4720 ·
9. 5739 ·
10. 59390
11. 60010
12. 60784
13. 72420
14. 73390
15. 78659
16. 78865
17. 79029
18. 79093 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
172
(98.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication6
[factor] |
1. 27240
2. 30401
3. 35180
4. 40190
5. 41400
6. 41519
7. 43812
8. 4720 ·
9. 5739 ·
10. 59390
11. 60010
12. 60784
13. 72420
14. 73390
15. 78659
16. 78865
17. 79029
18. 79093 |
| 2 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 2 | ( | 12.5% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) |
|
 |
274
(94.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication6
[factor] |
1. 27240
2. 30401
3. 35180
4. 40190
5. 41400
6. 41519
7. 43812
8. 4720 ·
9. 5739 ·
10. 59390
11. 60010
12. 60784
13. 72420
14. 73390
15. 78659
16. 78865
17. 79029
18. 79093 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication6
[factor] |
1. 27240
2. 30401
3. 35180
4. 40190
5. 41400
6. 41519
7. 43812
8. 4720 ·
9. 5739 ·
10. 59390
11. 60010
12. 60784
13. 72420
14. 73390
15. 78659
16. 78865
17. 79029
18. 79093 |
| 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
13
(81.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 7
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3161
All data
st_css() #IMPORTANT!
comorbidcomplication7 <- as.factor(d[,"comorbidcomplication7"])
new.d <- data.frame(new.d, comorbidcomplication7)
new.d <- apply_labels(new.d, comorbidcomplication7 = "comorbid_complication7")
#summary(new.d$comorbidcomplication7)
temp.d <- data.frame (new.d.1, comorbidcomplication7)
summarytools::view(dfSummary(new.d$comorbidcomplication7, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication7
[labelled, factor] |
comorbid_complication7 |
1. 25000
2. 26890
3. 27220
4. 27240
5. 27490
6. 27800
7. 30275
8. 32723
9. 41200
10. 58590
11. 72420
12. 78052
13. 78194
14. 78841
15. 79093
16. 81600
17. 9989 · |
| 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) | | 1 | ( | 5.9% | ) |
|
 |
2150
(99.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication7
[factor] |
1. 25000
2. 26890
3. 27220
4. 27240
5. 27490
6. 27800
7. 30275
8. 32723
9. 41200
10. 58590
11. 72420
12. 78052
13. 78194
14. 78841
15. 79093
16. 81600
17. 9989 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication7
[factor] |
1. 25000
2. 26890
3. 27220
4. 27240
5. 27490
6. 27800
7. 30275
8. 32723
9. 41200
10. 58590
11. 72420
12. 78052
13. 78194
14. 78841
15. 79093
16. 81600
17. 9989 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication7
[factor] |
1. 25000
2. 26890
3. 27220
4. 27240
5. 27490
6. 27800
7. 30275
8. 32723
9. 41200
10. 58590
11. 72420
12. 78052
13. 78194
14. 78841
15. 79093
16. 81600
17. 9989 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication7
[factor] |
1. 25000
2. 26890
3. 27220
4. 27240
5. 27490
6. 27800
7. 30275
8. 32723
9. 41200
10. 58590
11. 72420
12. 78052
13. 78194
14. 78841
15. 79093
16. 81600
17. 9989 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
172
(98.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication7
[factor] |
1. 25000
2. 26890
3. 27220
4. 27240
5. 27490
6. 27800
7. 30275
8. 32723
9. 41200
10. 58590
11. 72420
12. 78052
13. 78194
14. 78841
15. 79093
16. 81600
17. 9989 · |
| 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 8.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 0 | ( | 0.0% | ) |
|
 |
278
(95.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication7
[factor] |
1. 25000
2. 26890
3. 27220
4. 27240
5. 27490
6. 27800
7. 30275
8. 32723
9. 41200
10. 58590
11. 72420
12. 78052
13. 78194
14. 78841
15. 79093
16. 81600
17. 9989 · |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication7
[factor] |
1. 25000
2. 26890
3. 27220
4. 27240
5. 27490
6. 27800
7. 30275
8. 32723
9. 41200
10. 58590
11. 72420
12. 78052
13. 78194
14. 78841
15. 79093
16. 81600
17. 9989 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) |
|
 |
13
(81.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 8
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3162
All data
st_css() #IMPORTANT!
comorbidcomplication8 <- as.factor(d[,"comorbidcomplication8"])
new.d <- data.frame(new.d, comorbidcomplication8)
new.d <- apply_labels(new.d, comorbidcomplication8 = "comorbid_complication8")
#summary(new.d$comorbidcomplication8)
temp.d <- data.frame (new.d.1, comorbidcomplication8)
summarytools::view(dfSummary(new.d$comorbidcomplication8, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication8
[labelled, factor] |
comorbid_complication8 |
1. 25000
2. 26890
3. 28521
4. 33829
5. 41401
6. 53550
7. 56010
8. 56400
9. 71941
10. 75310
11. 78863
12. 79981 |
| 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 2 | ( | 15.4% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) | | 1 | ( | 7.7% | ) |
|
 |
2154
(99.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication8
[factor] |
1. 25000
2. 26890
3. 28521
4. 33829
5. 41401
6. 53550
7. 56010
8. 56400
9. 71941
10. 75310
11. 78863
12. 79981 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication8
[factor] |
1. 25000
2. 26890
3. 28521
4. 33829
5. 41401
6. 53550
7. 56010
8. 56400
9. 71941
10. 75310
11. 78863
12. 79981 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication8
[factor] |
1. 25000
2. 26890
3. 28521
4. 33829
5. 41401
6. 53550
7. 56010
8. 56400
9. 71941
10. 75310
11. 78863
12. 79981 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication8
[factor] |
1. 25000
2. 26890
3. 28521
4. 33829
5. 41401
6. 53550
7. 56010
8. 56400
9. 71941
10. 75310
11. 78863
12. 79981 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
172
(98.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication8
[factor] |
1. 25000
2. 26890
3. 28521
4. 33829
5. 41401
6. 53550
7. 56010
8. 56400
9. 71941
10. 75310
11. 78863
12. 79981 |
| 1 | ( | 12.5% | ) | | 1 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 12.5% | ) | | 1 | ( | 12.5% | ) | | 1 | ( | 12.5% | ) | | 1 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 12.5% | ) |
|
 |
282
(97.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication8
[factor] |
1. 25000
2. 26890
3. 28521
4. 33829
5. 41401
6. 53550
7. 56010
8. 56400
9. 71941
10. 75310
11. 78863
12. 79981 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication8
[factor] |
1. 25000
2. 26890
3. 28521
4. 33829
5. 41401
6. 53550
7. 56010
8. 56400
9. 71941
10. 75310
11. 78863
12. 79981 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
13
(81.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 9
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications during the patient’s hospital stay for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3163
All data
st_css() #IMPORTANT!
comorbidcomplication9 <- as.factor(d[,"comorbidcomplication9"])
new.d <- data.frame(new.d, comorbidcomplication9)
new.d <- apply_labels(new.d, comorbidcomplication9 = "comorbid_complication9")
#summary(new.d$comorbidcomplication9)
temp.d <- data.frame (new.d.1, comorbidcomplication9)
summarytools::view(dfSummary(new.d$comorbidcomplication9, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication9
[labelled, factor] |
comorbid_complication9 |
1. 32723
2. 41401
3. 60000
4. 71695
5. 72910
6. 73300
7. 78052 |
| 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) |
|
 |
2160
(99.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication9
[factor] |
1. 32723
2. 41401
3. 60000
4. 71695
5. 72910
6. 73300
7. 78052 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication9
[factor] |
1. 32723
2. 41401
3. 60000
4. 71695
5. 72910
6. 73300
7. 78052 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication9
[factor] |
1. 32723
2. 41401
3. 60000
4. 71695
5. 72910
6. 73300
7. 78052 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication9
[factor] |
1. 32723
2. 41401
3. 60000
4. 71695
5. 72910
6. 73300
7. 78052 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication9
[factor] |
1. 32723
2. 41401
3. 60000
4. 71695
5. 72910
6. 73300
7. 78052 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) |
|
 |
286
(98.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication9
[factor] |
1. 32723
2. 41401
3. 60000
4. 71695
5. 72910
6. 73300
7. 78052 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication9
[factor] |
1. 32723
2. 41401
3. 60000
4. 71695
5. 72910
6. 73300
7. 78052 |
| 1 | ( | 33.3% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
13
(81.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
COMORBID/COMPLICATION 10
Description: Records the patient’s pre-existing medical conditions, factors influencing health status, and/or complications for the treatment of this cancer using ICD-9-CM codes. All are considered secondary diagnoses.
Rationale: Pre-existing medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care.
Codes (Refer to the most recent version of STORE for additional instructions.) ICD-9-CM Codes 00100-13980, 24000-99990, E8700-E8799, E9300-E9499, V0720-V0739, V1000-V1590, V2220- V2310, V2540, V4400-V4589, and V5041-V5049.
Leave blank if no further secondary diagnosis.
Note: For comorbid conditions (ICD-9-CM codes 00100-13980 and 24000-99990), there is an assumed decimal point between the third and fourth characters. For complications (ICD-9-CM codes E8700-E8799 and E9300-E9499), there is an assumed decimal point between the fourth and fifth characters. For conditions influencing health status and contact with health services (ICD-9-CM codes V0720-V0739, V1000-V1590, V2220-V2310, V2540, V4400-V4589, and V5041-V5049), there is an assumed decimal point between the third and fourth characters.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3164
All data
st_css() #IMPORTANT!
comorbidcomplication10 <- as.factor(d[,"comorbidcomplication10"])
new.d <- data.frame(new.d, comorbidcomplication10)
new.d <- apply_labels(new.d, comorbidcomplication10 = "comorbid_complication10")
#summary(new.d$comorbidcomplication10)
temp.d <- data.frame (new.d.1, comorbidcomplication10)
summarytools::view(dfSummary(new.d$comorbidcomplication10, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbidcomplication10
[labelled, factor] |
comorbid_complication10 |
1. 25720
2. 33381
3. 4019 ·
4. 53560
5. 78841 |
| 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) |
|
 |
2162
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication10
[factor] |
1. 25720
2. 33381
3. 4019 ·
4. 53560
5. 78841 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication10
[factor] |
1. 25720
2. 33381
3. 4019 ·
4. 53560
5. 78841 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication10
[factor] |
1. 25720
2. 33381
3. 4019 ·
4. 53560
5. 78841 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication10
[factor] |
1. 25720
2. 33381
3. 4019 ·
4. 53560
5. 78841 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication10
[factor] |
1. 25720
2. 33381
3. 4019 ·
4. 53560
5. 78841 |
| 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) |
|
 |
286
(98.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication10
[factor] |
1. 25720
2. 33381
3. 4019 ·
4. 53560
5. 78841 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
comorbid_complication10
[factor] |
1. 25720
2. 33381
3. 4019 ·
4. 53560
5. 78841 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
15
(93.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
ICD REVISION COMORBID
Description: This item indicates the coding system in which the Comorbidities and Complications (secondary diagnoses) codes are provided.
Rationale: he CoC currently requires the collection and reporting of up to 10 ICD-9-CM codes describing secondary diagnoses for patients hospitalized for cancer treatment. Currently the use of ICD-10-CM is not mandatory in U.S. hospitals, though it may become so in the future. In the event this occurs cancer registries that maintain or collect this information will need to differentiate between ICD-9-CM and ICD-10-CM code use. The code values and definitions for this item would be expanded as necessary. Allowable codes reported in the Comorbidity and Complications items in STORE would be re-assessed at the same time.
Codes
- 0 No comorbidities or complications recorded in patient’s record
- 1 ICD-10-CM
- 9 ICD-9-CM
- Blank Comorbidities and Complications not collected
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3165
All data
st_css() #IMPORTANT!
icdrevisioncomorbid <- as.factor(d[,"icdrevisioncomorbid"])
levels(icdrevisioncomorbid) <- list(No_in_record.0="0",
ICD_10_CM.1 = "1",
ICD_9_CM.9 = "9"
)
new.d <- data.frame(new.d, icdrevisioncomorbid)
new.d <- apply_labels(new.d, icdrevisioncomorbid = "icd_revision_comorbid")
#summary(new.d$icdrevisioncomorbid)
temp.d <- data.frame (new.d.1, icdrevisioncomorbid)
summarytools::view(dfSummary(new.d$icdrevisioncomorbid, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
icdrevisioncomorbid
[labelled, factor] |
icd_revision_comorbid |
1. No_in_record.0
2. ICD_10_CM.1
3. ICD_9_CM.9 |
| 97 | ( | 35.9% | ) | | 142 | ( | 52.6% | ) | | 31 | ( | 11.5% | ) |
|
 |
1897
(87.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
icd_revision_comorbid
[factor] |
1. No_in_record.0
2. ICD_10_CM.1
3. ICD_9_CM.9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
icd_revision_comorbid
[factor] |
1. No_in_record.0
2. ICD_10_CM.1
3. ICD_9_CM.9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
icd_revision_comorbid
[factor] |
1. No_in_record.0
2. ICD_10_CM.1
3. ICD_9_CM.9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
icd_revision_comorbid
[factor] |
1. No_in_record.0
2. ICD_10_CM.1
3. ICD_9_CM.9 |
| 86 | ( | 51.2% | ) | | 55 | ( | 32.7% | ) | | 27 | ( | 16.1% | ) |
|
 |
6
(3.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
icd_revision_comorbid
[factor] |
1. No_in_record.0
2. ICD_10_CM.1
3. ICD_9_CM.9 |
|
 |
203
(70.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
icd_revision_comorbid
[factor] |
1. No_in_record.0
2. ICD_10_CM.1
3. ICD_9_CM.9 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
icd_revision_comorbid
[factor] |
1. No_in_record.0
2. ICD_10_CM.1
3. ICD_9_CM.9 |
|
 |
1
(6.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE MST DEFN SRG
- Description: Date of most definitive surgical resection of the primary site performed as part of the first course of treatment. See Chapter X for date format. Use RX DATE MST DEFN SRG FLAG [3171] if there is no appropriate or known date for this item.
Formerly RX Date–Most Defin Surg.
Rationale: This item is used to measure lag time between diagnosis and the most definitive surgery of the primary site or survival following the procedure. It also is used in conjunction with RX Date Surg Disch [3180] to calculate the duration of hospitalization following the most definitive primary site surgical procedure to evaluate treatment efficacy.
date var
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3170
All data
st_css() #IMPORTANT!
rxdatemostdefinsurg <- as.factor(d[,"rxdatemostdefinsurg"])
new.d <- data.frame(new.d, rxdatemostdefinsurg)
new.d <- apply_labels(new.d, rxdatemostdefinsurg = "rx_date_most_defin_surg")
#summary(new.d$rxdatemostdefinsurg)
temp.d <- data.frame (new.d.1, rxdatemostdefinsurg)
summarytools::view(dfSummary(new.d$rxdatemostdefinsurg, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdatemostdefinsurg
[labelled, factor] |
rx_date_most_defin_surg |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150527
43. 20150528
44. 20150599
45. 201506 ·
46. 20150603
47. 20150605
48. 20150610
49. 20150611
50. 20150612
[ 411 others ] |
| 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 5 | ( | 0.5% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 7 | ( | 0.7% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 5 | ( | 0.5% | ) | | 2 | ( | 0.2% | ) | | 6 | ( | 0.6% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 2 | ( | 0.2% | ) | | 2 | ( | 0.2% | ) | | 11 | ( | 1.1% | ) | | 2 | ( | 0.2% | ) | | 3 | ( | 0.3% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) | | 911 | ( | 89.9% | ) |
|
 |
1154
(53.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_most_defin_surg
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150527
43. 20150528
44. 20150599
45. 201506 ·
46. 20150603
47. 20150605
48. 20150610
49. 20150611
50. 20150612
[ 411 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 4.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 91 | ( | 86.7% | ) |
|
 |
84
(44.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_most_defin_surg
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150527
43. 20150528
44. 20150599
45. 201506 ·
46. 20150603
47. 20150605
48. 20150610
49. 20150611
50. 20150612
[ 411 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 45 | ( | 93.8% | ) |
|
 |
126
(72.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_most_defin_surg
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150527
43. 20150528
44. 20150599
45. 201506 ·
46. 20150603
47. 20150605
48. 20150610
49. 20150611
50. 20150612
[ 411 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 2.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 128 | ( | 88.9% | ) |
|
 |
93
(39.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_most_defin_surg
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150527
43. 20150528
44. 20150599
45. 201506 ·
46. 20150603
47. 20150605
48. 20150610
49. 20150611
50. 20150612
[ 411 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 78 | ( | 91.8% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_most_defin_surg
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150527
43. 20150528
44. 20150599
45. 201506 ·
46. 20150603
47. 20150605
48. 20150610
49. 20150611
50. 20150612
[ 411 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 159 | ( | 87.8% | ) |
|
 |
109
(37.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_most_defin_surg
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150527
43. 20150528
44. 20150599
45. 201506 ·
46. 20150603
47. 20150605
48. 20150610
49. 20150611
50. 20150612
[ 411 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.5% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 3 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.5% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 0.7% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.5% | ) | | 2 | ( | 0.5% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 410 | ( | 92.6% | ) |
|
 |
644
(59.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_most_defin_surg
[factor] |
1. 20120699
2. 20120999
3. 20130399
4. 20131199
5. 20140299
6. 20140599
7. 20141199
8. 201501 ·
9. 201502 ·
10. 20150212
11. 20150219
12. 20150223
13. 20150226
14. 201503 ·
15. 20150310
16. 20150316
17. 20150317
18. 20150326
19. 20150399
20. 201504 ·
21. 20150401
22. 20150402
23. 20150406
24. 20150407
25. 20150413
26. 20150414
27. 20150415
28. 20150416
29. 20150421
30. 20150423
31. 20150427
32. 20150428
33. 20150499
34. 201505 ·
35. 20150501
36. 20150504
37. 20150506
38. 20150507
39. 20150511
40. 20150514
41. 20150521
42. 20150527
43. 20150528
44. 20150599
45. 201506 ·
46. 20150603
47. 20150605
48. 20150610
49. 20150611
50. 20150612
[ 411 others ] |
| 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
9
(56.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RAD–BOOST RX MODALITY
Description: Records the dominant modality of radiation therapy used to deliver the most clinically significant boost dose to the primary volume of interest during the first course of treatment. This is accomplished with external beam fields of reduced size (relative to the regional treatment fields), implants, stereotactic radiosurgery, conformal therapy, or intensity-modulated radiation therapy. External beam boosts may consist of two or more successive phases with progressively smaller fields, and they are generally coded as a single entity. This field is used with Rad–Regional RX Modality [1570].
Rationale: Radiation treatment frequently is delivered in two or more phases that can be summarized as regional and boost treatments. A boost dose is administered to a volume within the regional volume. For outcomes analysis, the modalities used for each of these phases can be very important
Codes
- 00 No boost treatment
- 20 External beam, NOS
- 21 Orthovoltage
- 22 Cobalt-60, Cesium-137
- 23 Photons (2-5 MV)
- 24 Photons (6-10 MV)
- 25 Photons (11-19 MV)
- 26 Photons (> 19 MV)
- 27 Photons (mixed energies)
- 28 Electrons
- 29 Photons and electrons mixed
- 30 Neutrons, with or without photons/electrons
- 31 IMRT
- 32 Conformal or 3-D therapy
- 40 Protons
- 41 Stereotactic radiosurgery, NOS
- 42 Linac radiosurgery
- 43 Gamma Knife
- 50 Brachytherapy, NOS
- 51 Brachytherapy, Intracavitary, LDR
- 52 Brachytherapy, Intracavitary, HDR
- 53 Brachytherapy, Interstitial, LDR
- 54 Brachytherapy, Interstitial, HDR
- 55 Radium
- 60 Radio-isotopes, NOS
- 61 Strontium - 89
- 62 Strontium - 90
- 98 Other, NOS
- 99 Unknown
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3200
All data
st_css() #IMPORTANT!
radboostrxmodality <- as.factor(d[,"radboostrxmodality"])
levels(radboostrxmodality) <- list(No_boost_tx.0="0",
External_beam_NOS.20="20",
Photons_6_10_MV.24="24",
Photons_11_19_MV.25="25",
Photons_mixed.27="27",
Electrons.28 = "28",
IMRT.31 = "31",
Conformal_or_3D.32="32",
Protons.40="40",
Brachytherapy_NOS.50="50",
Brachy_Intracavitary_LDR.51="51",
Brachy_Intracavitary_HDR.52="52",
Brachy_Interstitial_LDR.53="53",
Brachy_Interstitial_HDR.54="54",
Unknown.99 = "99"
)
new.d <- data.frame(new.d, radboostrxmodality)
new.d <- apply_labels(new.d, radboostrxmodality = "rad_boost_rx_modality")
#summary(new.d$radboostrxmodality)
temp.d <- data.frame (new.d.1, radboostrxmodality)
summarytools::view(dfSummary(new.d$radboostrxmodality, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
radboostrxmodality
[labelled, factor] |
rad_boost_rx_modality |
1. No_boost_tx.0
2. External_beam_NOS.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_mixed.27
6. Electrons.28
7. IMRT.31
8. Conformal_or_3D.32
9. Protons.40
10. Brachytherapy_NOS.50
11. Brachy_Intracavitary_LDR.
12. Brachy_Intracavitary_HDR.
13. Brachy_Interstitial_LDR.5
14. Brachy_Interstitial_HDR.5
15. Unknown.99 |
| 0 | ( | 0.0% | ) | | 14 | ( | 4.6% | ) | | 23 | ( | 7.5% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 126 | ( | 41.0% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 10 | ( | 3.3% | ) | | 3 | ( | 1.0% | ) | | 2 | ( | 0.7% | ) | | 95 | ( | 30.9% | ) | | 14 | ( | 4.6% | ) | | 13 | ( | 4.2% | ) |
|
 |
1860
(85.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_boost_tx.0
2. External_beam_NOS.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_mixed.27
6. Electrons.28
7. IMRT.31
8. Conformal_or_3D.32
9. Protons.40
10. Brachytherapy_NOS.50
11. Brachy_Intracavitary_LDR.51
12. Brachy_Intracavitary_HDR.52
13. Brachy_Interstitial_LDR.53
14. Brachy_Interstitial_HDR.54
15. Unknown.99 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_boost_tx.0
2. External_beam_NOS.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_mixed.27
6. Electrons.28
7. IMRT.31
8. Conformal_or_3D.32
9. Protons.40
10. Brachytherapy_NOS.50
11. Brachy_Intracavitary_LDR.51
12. Brachy_Intracavitary_HDR.52
13. Brachy_Interstitial_LDR.53
14. Brachy_Interstitial_HDR.54
15. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_boost_tx.0
2. External_beam_NOS.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_mixed.27
6. Electrons.28
7. IMRT.31
8. Conformal_or_3D.32
9. Protons.40
10. Brachytherapy_NOS.50
11. Brachy_Intracavitary_LDR.51
12. Brachy_Intracavitary_HDR.52
13. Brachy_Interstitial_LDR.53
14. Brachy_Interstitial_HDR.54
15. Unknown.99 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_boost_tx.0
2. External_beam_NOS.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_mixed.27
6. Electrons.28
7. IMRT.31
8. Conformal_or_3D.32
9. Protons.40
10. Brachytherapy_NOS.50
11. Brachy_Intracavitary_LDR.51
12. Brachy_Intracavitary_HDR.52
13. Brachy_Interstitial_LDR.53
14. Brachy_Interstitial_HDR.54
15. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_boost_tx.0
2. External_beam_NOS.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_mixed.27
6. Electrons.28
7. IMRT.31
8. Conformal_or_3D.32
9. Protons.40
10. Brachytherapy_NOS.50
11. Brachy_Intracavitary_LDR.
12. Brachy_Intracavitary_HDR.
13. Brachy_Interstitial_LDR.5
14. Brachy_Interstitial_HDR.5
15. Unknown.99 |
| 0 | ( | 0.0% | ) | | 7 | ( | 11.5% | ) | | 5 | ( | 8.2% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 33 | ( | 54.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.3% | ) | | 3 | ( | 4.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 8 | ( | 13.1% | ) |
|
 |
229
(79.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_boost_tx.0
2. External_beam_NOS.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_mixed.27
6. Electrons.28
7. IMRT.31
8. Conformal_or_3D.32
9. Protons.40
10. Brachytherapy_NOS.50
11. Brachy_Intracavitary_LDR.
12. Brachy_Intracavitary_HDR.
13. Brachy_Interstitial_LDR.5
14. Brachy_Interstitial_HDR.5
15. Unknown.99 |
| 0 | ( | 0.0% | ) | | 7 | ( | 2.8% | ) | | 18 | ( | 7.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 93 | ( | 37.8% | ) | | 2 | ( | 0.8% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 2.8% | ) | | 3 | ( | 1.2% | ) | | 2 | ( | 0.8% | ) | | 94 | ( | 38.2% | ) | | 13 | ( | 5.3% | ) | | 5 | ( | 2.0% | ) |
|
 |
841
(77.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_boost_tx.0
2. External_beam_NOS.20
3. Photons_6_10_MV.24
4. Photons_11_19_MV.25
5. Photons_mixed.27
6. Electrons.28
7. IMRT.31
8. Conformal_or_3D.32
9. Protons.40
10. Brachytherapy_NOS.50
11. Brachy_Intracavitary_LDR.51
12. Brachy_Intracavitary_HDR.52
13. Brachy_Interstitial_LDR.53
14. Brachy_Interstitial_HDR.54
15. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RAD–BOOST DOSE CGY
Description: Records the additional dose delivered to that part of the treatment volume encompassed by the boost fields or devices. The unit of measure is centiGray (cGy).
Rationale: To evaluate patterns of radiation oncology care, it is necessary to describe the boost radiation dose. A boost dose is administered to a volume within the regional volume. As in chemotherapy, outcomes are strongly related to the dose delivered.
Codes
- (Fill blanks) Record the actual boost dose delivered
- 00000 Boost radiation therapy was not administered
- 88888 Not applicable, brachytherapy or radioisotopes administered to the patient
- 99999 Boost radiation therapy administered, boost dose unknown
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3210
All data
st_css() #IMPORTANT!
radboostdosecgy <- as.factor(d[,"radboostdosecgy"])
new.d <- data.frame(new.d, radboostdosecgy)
new.d <- apply_labels(new.d, radboostdosecgy = "rad_boost_dose_cgy")
#summary(new.d$radboostdosecgy)
temp.d <- data.frame (new.d.1, radboostdosecgy)
summarytools::view(dfSummary(new.d$radboostdosecgy, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
radboostdosecgy
[labelled, factor] |
rad_boost_dose_cgy |
No levels defined |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX DATE SYSTEMIC
- Description: Date of initiation of systemic therapy that is part of the first course of treatment. Systemic therapy includes the administration of chemotherapy agents, hormone agents, biological response modifiers, bone marrow transplants, stem cell harvests, and surgical and/or radiation endocrine therapy. See Chapter X for date format. Use RX DATE SYSTEMIC FLAG [3231] if there is no appropriate or known date for this item.
Formerly RX Date–Systemic.
All data
st_css() #IMPORTANT!
rxdatesystemic <- as.factor(d[,"rxdatesystemic"])
new.d <- data.frame(new.d, rxdatesystemic)
new.d <- apply_labels(new.d, rxdatesystemic = "rx_date_systemic")
#summary(new.d$rxdatesystemic)
temp.d <- data.frame (new.d.1, rxdatesystemic)
summarytools::view(dfSummary(new.d$rxdatesystemic, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxdatesystemic
[labelled, factor] |
rx_date_systemic |
1. 20130499
2. 2015 ·
3. 201501 ·
4. 20150116
5. 20150126
6. 201502 ·
7. 20150210
8. 20150211
9. 20150217
10. 201503 ·
11. 20150306
12. 20150326
13. 20150399
14. 201504 ·
15. 20150401
16. 20150410
17. 20150415
18. 20150428
19. 20150499
20. 201505 ·
21. 20150504
22. 20150505
23. 20150507
24. 20150509
25. 20150514
26. 20150515
27. 20150519
28. 20150521
29. 20150526
30. 20150528
31. 201506 ·
32. 20150601
33. 20150602
34. 20150604
35. 20150608
36. 20150611
37. 20150612
38. 20150615
39. 20150616
40. 20150618
41. 20150619
42. 20150623
43. 20150625
44. 20150626
45. 20150627
46. 20150699
47. 20150702
48. 20150709
49. 20150727
50. 20150799
[ 237 others ] |
| 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 5 | ( | 1.1% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 6 | ( | 1.3% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 2 | ( | 0.4% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 1 | ( | 0.2% | ) | | 406 | ( | 85.8% | ) |
|
 |
1694
(78.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_systemic
[factor] |
1. 20130499
2. 2015 ·
3. 201501 ·
4. 20150116
5. 20150126
6. 201502 ·
7. 20150210
8. 20150211
9. 20150217
10. 201503 ·
11. 20150306
12. 20150326
13. 20150399
14. 201504 ·
15. 20150401
16. 20150410
17. 20150415
18. 20150428
19. 20150499
20. 201505 ·
21. 20150504
22. 20150505
23. 20150507
24. 20150509
25. 20150514
26. 20150515
27. 20150519
28. 20150521
29. 20150526
30. 20150528
31. 201506 ·
32. 20150601
33. 20150602
34. 20150604
35. 20150608
36. 20150611
37. 20150612
38. 20150615
39. 20150616
40. 20150618
41. 20150619
42. 20150623
43. 20150625
44. 20150626
45. 20150627
46. 20150699
47. 20150702
48. 20150709
49. 20150727
50. 20150799
[ 237 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 32 | ( | 86.5% | ) |
|
 |
152
(80.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_systemic
[factor] |
1. 20130499
2. 2015 ·
3. 201501 ·
4. 20150116
5. 20150126
6. 201502 ·
7. 20150210
8. 20150211
9. 20150217
10. 201503 ·
11. 20150306
12. 20150326
13. 20150399
14. 201504 ·
15. 20150401
16. 20150410
17. 20150415
18. 20150428
19. 20150499
20. 201505 ·
21. 20150504
22. 20150505
23. 20150507
24. 20150509
25. 20150514
26. 20150515
27. 20150519
28. 20150521
29. 20150526
30. 20150528
31. 201506 ·
32. 20150601
33. 20150602
34. 20150604
35. 20150608
36. 20150611
37. 20150612
38. 20150615
39. 20150616
40. 20150618
41. 20150619
42. 20150623
43. 20150625
44. 20150626
45. 20150627
46. 20150699
47. 20150702
48. 20150709
49. 20150727
50. 20150799
[ 237 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 42 | ( | 91.3% | ) |
|
 |
128
(73.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_systemic
[factor] |
1. 20130499
2. 2015 ·
3. 201501 ·
4. 20150116
5. 20150126
6. 201502 ·
7. 20150210
8. 20150211
9. 20150217
10. 201503 ·
11. 20150306
12. 20150326
13. 20150399
14. 201504 ·
15. 20150401
16. 20150410
17. 20150415
18. 20150428
19. 20150499
20. 201505 ·
21. 20150504
22. 20150505
23. 20150507
24. 20150509
25. 20150514
26. 20150515
27. 20150519
28. 20150521
29. 20150526
30. 20150528
31. 201506 ·
32. 20150601
33. 20150602
34. 20150604
35. 20150608
36. 20150611
37. 20150612
38. 20150615
39. 20150616
40. 20150618
41. 20150619
42. 20150623
43. 20150625
44. 20150626
45. 20150627
46. 20150699
47. 20150702
48. 20150709
49. 20150727
50. 20150799
[ 237 others ] |
| 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 6.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 38 | ( | 84.4% | ) |
|
 |
192
(81.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_systemic
[factor] |
1. 20130499
2. 2015 ·
3. 201501 ·
4. 20150116
5. 20150126
6. 201502 ·
7. 20150210
8. 20150211
9. 20150217
10. 201503 ·
11. 20150306
12. 20150326
13. 20150399
14. 201504 ·
15. 20150401
16. 20150410
17. 20150415
18. 20150428
19. 20150499
20. 201505 ·
21. 20150504
22. 20150505
23. 20150507
24. 20150509
25. 20150514
26. 20150515
27. 20150519
28. 20150521
29. 20150526
30. 20150528
31. 201506 ·
32. 20150601
33. 20150602
34. 20150604
35. 20150608
36. 20150611
37. 20150612
38. 20150615
39. 20150616
40. 20150618
41. 20150619
42. 20150623
43. 20150625
44. 20150626
45. 20150627
46. 20150699
47. 20150702
48. 20150709
49. 20150727
50. 20150799
[ 237 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.3% | ) | | 39 | ( | 90.7% | ) |
|
 |
131
(75.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_systemic
[factor] |
1. 20130499
2. 2015 ·
3. 201501 ·
4. 20150116
5. 20150126
6. 201502 ·
7. 20150210
8. 20150211
9. 20150217
10. 201503 ·
11. 20150306
12. 20150326
13. 20150399
14. 201504 ·
15. 20150401
16. 20150410
17. 20150415
18. 20150428
19. 20150499
20. 201505 ·
21. 20150504
22. 20150505
23. 20150507
24. 20150509
25. 20150514
26. 20150515
27. 20150519
28. 20150521
29. 20150526
30. 20150528
31. 201506 ·
32. 20150601
33. 20150602
34. 20150604
35. 20150608
36. 20150611
37. 20150612
38. 20150615
39. 20150616
40. 20150618
41. 20150619
42. 20150623
43. 20150625
44. 20150626
45. 20150627
46. 20150699
47. 20150702
48. 20150709
49. 20150727
50. 20150799
[ 237 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.3% | ) | | 1 | ( | 1.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 62 | ( | 80.5% | ) |
|
 |
213
(73.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_systemic
[factor] |
1. 20130499
2. 2015 ·
3. 201501 ·
4. 20150116
5. 20150126
6. 201502 ·
7. 20150210
8. 20150211
9. 20150217
10. 201503 ·
11. 20150306
12. 20150326
13. 20150399
14. 201504 ·
15. 20150401
16. 20150410
17. 20150415
18. 20150428
19. 20150499
20. 201505 ·
21. 20150504
22. 20150505
23. 20150507
24. 20150509
25. 20150514
26. 20150515
27. 20150519
28. 20150521
29. 20150526
30. 20150528
31. 201506 ·
32. 20150601
33. 20150602
34. 20150604
35. 20150608
36. 20150611
37. 20150612
38. 20150615
39. 20150616
40. 20150618
41. 20150619
42. 20150623
43. 20150625
44. 20150626
45. 20150627
46. 20150699
47. 20150702
48. 20150709
49. 20150727
50. 20150799
[ 237 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.9% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.9% | ) | | 1 | ( | 0.4% | ) | | 2 | ( | 0.9% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.4% | ) | | 0 | ( | 0.0% | ) | | 193 | ( | 86.2% | ) |
|
 |
863
(79.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rx_date_systemic
[factor] |
1. 20130499
2. 2015 ·
3. 201501 ·
4. 20150116
5. 20150126
6. 201502 ·
7. 20150210
8. 20150211
9. 20150217
10. 201503 ·
11. 20150306
12. 20150326
13. 20150399
14. 201504 ·
15. 20150401
16. 20150410
17. 20150415
18. 20150428
19. 20150499
20. 201505 ·
21. 20150504
22. 20150505
23. 20150507
24. 20150509
25. 20150514
26. 20150515
27. 20150519
28. 20150521
29. 20150526
30. 20150528
31. 201506 ·
32. 20150601
33. 20150602
34. 20150604
35. 20150608
36. 20150611
37. 20150612
38. 20150615
39. 20150616
40. 20150618
41. 20150619
42. 20150623
43. 20150625
44. 20150626
45. 20150627
46. 20150699
47. 20150702
48. 20150709
49. 20150727
50. 20150799
[ 237 others ] |
| 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
15
(93.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
RX SUMM–TRANSPLNT/ENDOCR
Description: Identifies systemic therapeutic procedures administered as part of the first course of treatment at this and all other facilities. If none of these procedures were administered then this item records the reason they were not performed. These include bone marrow transplants, stem cell harvests, surgical and/or radiation endocrine therapy.
Rationale: This data item allows the evaluation of patterns of treatment, which involve the alteration of the immune system or change the patient’s response to tumor cells but do not involve the administration of antineoplastic agents.
Codes
- 00 No transplant procedure or endocrine therapy was administered as part of first course therapy; diagnosed at autopsy
- 10 Bone marrow transplant procedure was administered, but the type was not specified.
- 11 Bone marrow transplant-autologous
- 12 Bone marrow transplant-allogeneic
- 20 Stem cell harvest and infusion
- 30 Endocrine surgery and/or endocrine radiation therapy.
- 40 Combination of endocrine surgery and/or radiation with a transplant procedure. (combination of codes 30 and 10, 11, 12 or 20).
- 82 Hematologic transplant and/or endocrine surgery/radiation was not recommended/administered because it was contraindicated due to patient risk factors (i.e., comorbid conditions, advanced age).
- 85 Hematologic transplant and/or endocrine surgery/radiation was not administered because the patient died prior to planned or recommended therapy.
- 86 Hematologic transplant and/or endocrine surgery/radiation was not administered. It was recommended by the patient’s physician, but was not administered as part of first-course therapy. No reason was stated in the patient record.
- 87 Hematologic transplant and/or endocrine surgery/radiation was not administered; it was recommended by the patient’s physician, but this treatment was refused by the patient, the patient’s family member, or the patient’s guardian; refusal noted in patient record
- 88 Hematologic transplant and/or endocrine surgery/radiation was recommended, but it is unknown if it was administered
- 99 It is unknown whether hematologic transplant and/or endocrine surgery/radiation was recommended or administered because it is not stated in patient record; death certificate-only cases
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3250
All data
st_css() #IMPORTANT!
rxsummtransplntendocr <- as.factor(d[,"rxsummtransplntendocr"])
levels(rxsummtransplntendocr) <- list(No_administered.0="0",
Endo_surg_RT.30 = "30",
Recomm_no_admin.86="86",
Unknown.99 = "99"
)
new.d <- data.frame(new.d, rxsummtransplntendocr)
new.d <- apply_labels(new.d, rxsummtransplntendocr = "rx_summ_transplnt_endocr")
#summary(new.d$rxsummtransplntendocr)
temp.d <- data.frame (new.d.1, rxsummtransplntendocr)
summarytools::view(dfSummary(new.d$rxsummtransplntendocr, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rxsummtransplntendocr
[labelled, factor] |
rx_summ_transplnt_endocr |
1. No_administered.0
2. Endo_surg_RT.30
3. Recomm_no_admin.86
4. Unknown.99 |
| 0 | ( | 0.0% | ) | | 4 | ( | 25.0% | ) | | 1 | ( | 6.2% | ) | | 11 | ( | 68.8% | ) |
|
 |
2151
(99.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_administered.0
2. Endo_surg_RT.30
3. Recomm_no_admin.86
4. Unknown.99 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_administered.0
2. Endo_surg_RT.30
3. Recomm_no_admin.86
4. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_administered.0
2. Endo_surg_RT.30
3. Recomm_no_admin.86
4. Unknown.99 |
| 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
236
(99.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_administered.0
2. Endo_surg_RT.30
3. Recomm_no_admin.86
4. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_administered.0
2. Endo_surg_RT.30
3. Recomm_no_admin.86
4. Unknown.99 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 100.0% | ) |
|
 |
279
(96.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_administered.0
2. Endo_surg_RT.30
3. Recomm_no_admin.86
4. Unknown.99 |
| 0 | ( | 0.0% | ) | | 3 | ( | 75.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1083
(99.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
rad_boost_rx_modality
[factor] |
1. No_administered.0
2. Endo_surg_RT.30
3. Recomm_no_admin.86
4. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED AJCC-7 T
All data
st_css() #IMPORTANT!
derivedajcc7t <- as.factor(d[,"derivedajcc7t"])
new.d <- data.frame(new.d, derivedajcc7t)
new.d <- apply_labels(new.d, derivedajcc7t = "derived_ajcc_7t")
#summary(new.d$derivedajcc7t)
temp.d <- data.frame (new.d.1, derivedajcc7t)
summarytools::view(dfSummary(new.d$derivedajcc7t, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedajcc7t
[labelled, factor] |
derived_ajcc_7t |
1. 120
2. 150
3. 180
4. 199
5. 210
6. 220
7. 230
8. 299
9. 310
10. 320
11. 399
12. 400
13. 999 |
| 5 | ( | 0.3% | ) | | 7 | ( | 0.4% | ) | | 608 | ( | 38.3% | ) | | 2 | ( | 0.1% | ) | | 64 | ( | 4.0% | ) | | 25 | ( | 1.6% | ) | | 440 | ( | 27.7% | ) | | 199 | ( | 12.5% | ) | | 111 | ( | 7.0% | ) | | 88 | ( | 5.5% | ) | | 10 | ( | 0.6% | ) | | 10 | ( | 0.6% | ) | | 20 | ( | 1.3% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7t
[factor] |
1. 120
2. 150
3. 180
4. 199
5. 210
6. 220
7. 230
8. 299
9. 310
10. 320
11. 399
12. 400
13. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 20 | ( | 22.2% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 5.6% | ) | | 5 | ( | 5.6% | ) | | 38 | ( | 42.2% | ) | | 1 | ( | 1.1% | ) | | 13 | ( | 14.4% | ) | | 7 | ( | 7.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7t
[factor] |
1. 120
2. 150
3. 180
4. 199
5. 210
6. 220
7. 230
8. 299
9. 310
10. 320
11. 399
12. 400
13. 999 |
| 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 45 | ( | 52.9% | ) | | 1 | ( | 1.2% | ) | | 8 | ( | 9.4% | ) | | 3 | ( | 3.5% | ) | | 11 | ( | 12.9% | ) | | 7 | ( | 8.2% | ) | | 7 | ( | 8.2% | ) | | 1 | ( | 1.2% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7t
[factor] |
1. 120
2. 150
3. 180
4. 199
5. 210
6. 220
7. 230
8. 299
9. 310
10. 320
11. 399
12. 400
13. 999 |
| 2 | ( | 1.4% | ) | | 1 | ( | 0.7% | ) | | 48 | ( | 34.5% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 5.0% | ) | | 2 | ( | 1.4% | ) | | 47 | ( | 33.8% | ) | | 8 | ( | 5.8% | ) | | 12 | ( | 8.6% | ) | | 10 | ( | 7.2% | ) | | 1 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.7% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7t
[factor] |
1. 120
2. 150
3. 180
4. 199
5. 210
6. 220
7. 230
8. 299
9. 310
10. 320
11. 399
12. 400
13. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 16.9% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.4% | ) | | 1 | ( | 1.7% | ) | | 28 | ( | 47.5% | ) | | 6 | ( | 10.2% | ) | | 6 | ( | 10.2% | ) | | 4 | ( | 6.8% | ) | | 1 | ( | 1.7% | ) | | 1 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7t
[factor] |
1. 120
2. 150
3. 180
4. 199
5. 210
6. 220
7. 230
8. 299
9. 310
10. 320
11. 399
12. 400
13. 999 |
| 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 27 | ( | 23.5% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 6.1% | ) | | 2 | ( | 1.7% | ) | | 46 | ( | 40.0% | ) | | 12 | ( | 10.4% | ) | | 11 | ( | 9.6% | ) | | 7 | ( | 6.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.9% | ) | | 0 | ( | 0.0% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7t
[factor] |
1. 120
2. 150
3. 180
4. 199
5. 210
6. 220
7. 230
8. 299
9. 310
10. 320
11. 399
12. 400
13. 999 |
| 2 | ( | 0.2% | ) | | 4 | ( | 0.4% | ) | | 453 | ( | 41.8% | ) | | 1 | ( | 0.1% | ) | | 35 | ( | 3.2% | ) | | 12 | ( | 1.1% | ) | | 265 | ( | 24.4% | ) | | 160 | ( | 14.7% | ) | | 61 | ( | 5.6% | ) | | 59 | ( | 5.4% | ) | | 7 | ( | 0.6% | ) | | 7 | ( | 0.6% | ) | | 19 | ( | 1.8% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7t
[factor] |
1. 120
2. 150
3. 180
4. 199
5. 210
6. 220
7. 230
8. 299
9. 310
10. 320
11. 399
12. 400
13. 999 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 31.2% | ) | | 5 | ( | 31.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED AJCC-7 N
All data
st_css() #IMPORTANT!
derivedajcc7n <- as.factor(d[,"derivedajcc7n"])
new.d <- data.frame(new.d, derivedajcc7n)
new.d <- apply_labels(new.d, derivedajcc7n = "derived_ajcc_7n")
#summary(new.d$derivedajcc7n)
temp.d <- data.frame (new.d.1, derivedajcc7n)
summarytools::view(dfSummary(new.d$derivedajcc7n, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedajcc7n
[labelled, factor] |
derived_ajcc_7n |
1. 0 ·
2. 100
3. 999 |
| 1496 | ( | 94.1% | ) | | 65 | ( | 4.1% | ) | | 28 | ( | 1.8% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7n
[factor] |
1. 0 ·
2. 100
3. 999 |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7n
[factor] |
1. 0 ·
2. 100
3. 999 |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7n
[factor] |
1. 0 ·
2. 100
3. 999 |
| 125 | ( | 89.9% | ) | | 11 | ( | 7.9% | ) | | 3 | ( | 2.2% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7n
[factor] |
1. 0 ·
2. 100
3. 999 |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7n
[factor] |
1. 0 ·
2. 100
3. 999 |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7n
[factor] |
1. 0 ·
2. 100
3. 999 |
| 1023 | ( | 94.3% | ) | | 38 | ( | 3.5% | ) | | 24 | ( | 2.2% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7n
[factor] |
1. 0 ·
2. 100
3. 999 |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED AJCC-7 M
All data
st_css() #IMPORTANT!
derivedajcc7m <- as.factor(d[,"derivedajcc7m"])
new.d <- data.frame(new.d, derivedajcc7m)
new.d <- apply_labels(new.d, derivedajcc7m = "derived_ajcc_7m")
#summary(new.d$derivedajcc7m)
temp.d <- data.frame (new.d.1, derivedajcc7m)
summarytools::view(dfSummary(new.d$derivedajcc7m, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedajcc7m
[labelled, factor] |
derived_ajcc_7m |
1. 0 ·
2. 110
3. 120
4. 130
5. 199 |
| 1550 | ( | 97.5% | ) | | 7 | ( | 0.4% | ) | | 30 | ( | 1.9% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) |
|
 |
578
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7m
[factor] |
1. 0 ·
2. 110
3. 120
4. 130
5. 199 |
| 88 | ( | 97.8% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7m
[factor] |
1. 0 ·
2. 110
3. 120
4. 130
5. 199 |
| 83 | ( | 97.6% | ) | | 1 | ( | 1.2% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7m
[factor] |
1. 0 ·
2. 110
3. 120
4. 130
5. 199 |
| 136 | ( | 97.8% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7m
[factor] |
1. 0 ·
2. 110
3. 120
4. 130
5. 199 |
| 56 | ( | 94.9% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 5.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7m
[factor] |
1. 0 ·
2. 110
3. 120
4. 130
5. 199 |
| 113 | ( | 98.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7m
[factor] |
1. 0 ·
2. 110
3. 120
4. 130
5. 199 |
| 1058 | ( | 97.5% | ) | | 5 | ( | 0.5% | ) | | 20 | ( | 1.8% | ) | | 1 | ( | 0.1% | ) | | 1 | ( | 0.1% | ) |
|
 |
2
(0.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7m
[factor] |
1. 0 ·
2. 110
3. 120
4. 130
5. 199 |
| 16 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED AJCC-7 STAGE GRP
All data
st_css() #IMPORTANT!
derivedajcc7stagegrp <- as.factor(d[,"derivedajcc7stagegrp"])
new.d <- data.frame(new.d, derivedajcc7stagegrp)
new.d <- apply_labels(new.d, derivedajcc7stagegrp = "derived_ajcc_7_stage_grp")
#summary(new.d$derivedajcc7stagegrp)
temp.d <- data.frame (new.d.1, derivedajcc7stagegrp)
summarytools::view(dfSummary(new.d$derivedajcc7stagegrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedajcc7stagegrp
[labelled, factor] |
derived_ajcc_7_stage_grp |
1. 100
2. 320
3. 330
4. 500
5. 700
6. 999 |
| 196 | ( | 12.3% | ) | | 411 | ( | 25.9% | ) | | 678 | ( | 42.7% | ) | | 171 | ( | 10.8% | ) | | 96 | ( | 6.0% | ) | | 36 | ( | 2.3% | ) |
|
 |
579
(26.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7_stage_grp
[factor] |
1. 100
2. 320
3. 330
4. 500
5. 700
6. 999 |
| 2 | ( | 2.2% | ) | | 22 | ( | 24.4% | ) | | 43 | ( | 47.8% | ) | | 16 | ( | 17.8% | ) | | 7 | ( | 7.8% | ) | | 0 | ( | 0.0% | ) |
|
 |
99
(52.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7_stage_grp
[factor] |
1. 100
2. 320
3. 330
4. 500
5. 700
6. 999 |
| 29 | ( | 34.1% | ) | | 23 | ( | 27.1% | ) | | 22 | ( | 25.9% | ) | | 8 | ( | 9.4% | ) | | 3 | ( | 3.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
89
(51.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7_stage_grp
[factor] |
1. 100
2. 320
3. 330
4. 500
5. 700
6. 999 |
| 23 | ( | 16.5% | ) | | 20 | ( | 14.4% | ) | | 65 | ( | 46.8% | ) | | 16 | ( | 11.5% | ) | | 13 | ( | 9.4% | ) | | 2 | ( | 1.4% | ) |
|
 |
98
(41.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7_stage_grp
[factor] |
1. 100
2. 320
3. 330
4. 500
5. 700
6. 999 |
| 2 | ( | 3.4% | ) | | 9 | ( | 15.3% | ) | | 34 | ( | 57.6% | ) | | 9 | ( | 15.3% | ) | | 5 | ( | 8.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
115
(66.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7_stage_grp
[factor] |
1. 100
2. 320
3. 330
4. 500
5. 700
6. 999 |
| 3 | ( | 2.6% | ) | | 12 | ( | 10.4% | ) | | 76 | ( | 66.1% | ) | | 15 | ( | 13.0% | ) | | 8 | ( | 7.0% | ) | | 1 | ( | 0.9% | ) |
|
 |
175
(60.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7_stage_grp
[factor] |
1. 100
2. 320
3. 330
4. 500
5. 700
6. 999 |
| 132 | ( | 12.2% | ) | | 321 | ( | 29.6% | ) | | 432 | ( | 39.9% | ) | | 106 | ( | 9.8% | ) | | 60 | ( | 5.5% | ) | | 33 | ( | 3.0% | ) |
|
 |
3
(0.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_ajcc_7_stage_grp
[factor] |
1. 100
2. 320
3. 330
4. 500
5. 700
6. 999 |
| 5 | ( | 31.2% | ) | | 4 | ( | 25.0% | ) | | 6 | ( | 37.5% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
0
(0.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SEER PATH STG GRP
Description: This data item is needed to store the results of the derived algorithmic calculation of Derived SEER Pathologic Stage Group.
Rationale: The SEER Program is developing an algorithm to calculate clinical and pathologic stage group based on their T, N, and M components and additional information as needed to calculate stage. For example, for thyroid, additional information is needed on histology and age to calculate stage. Once the T, N, and M are known an algorithm can assign the stage group instead of a registrar having to look up the stage. There are also provisions for a separate field for directly assigned stage group if the registrar prefers entering it.
Codes
- 0 Stage 0
- 0A Stage 0A
- 0IS Stage 0is
- 1 Stage I
- 1A Stage IA
- 1A1 Stage IA1
- 1A2 Stage IA2
- 1B Stage IB
- 1B1 Stage IB1
- 1B2 Stage IB2
- 1C Stage IC
- 1S Stage IS
- 2 Stage II
- 2A Stage IIA
- 2A1 Stage IIA1
- 2A2 Stage IIA2
- 2B Stage IIB
- 2C Stage IIC
- 3 Stage III
- 3A Stage IIIA
- 3B Stage IIIB
- 3C Stage IIIC
- 3C1 Stage IIIC1
- 3C2 Stage IIIC2
- 4 Stage IV
- 4A Stage IVA
- 4A1 Stage IVA1
- 4A2 Stage IVA2
- 4B Stage IVB
- 4C Stage IVC
- OC Occult
- 88 Not applicable
- 99 Unknown
- Blank Algorithm has not been run
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3605
All data
st_css() #IMPORTANT!
derivedseerpathstggrp <- as.factor(d[,"derivedseerpathstggrp"])
levels(derivedseerpathstggrp) <- list(Stage_I.1="1",
Stage_IIA.2A="2A",
Stage_IIB.2B="2B",
Stage_III.3="3",
Stage_IV.4="4",
Unknown.99="99")
new.d <- data.frame(new.d, derivedseerpathstggrp)
new.d <- apply_labels(new.d, derivedseerpathstggrp = "derived_seer_path_stg_grp")
#summary(new.d$derivedseerpathstggrp)
temp.d <- data.frame (new.d.1, derivedseerpathstggrp)
summarytools::view(dfSummary(new.d$derivedseerpathstggrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedseerpathstggrp
[labelled, factor] |
derived_seer_path_stg_grp |
1. Stage_I.1
2. Stage_IIA.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Unknown.99 |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_path_stg_grp
[factor] |
1. Stage_I.1
2. Stage_IIA.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Unknown.99 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_path_stg_grp
[factor] |
1. Stage_I.1
2. Stage_IIA.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_path_stg_grp
[factor] |
1. Stage_I.1
2. Stage_IIA.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Unknown.99 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_path_stg_grp
[factor] |
1. Stage_I.1
2. Stage_IIA.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_path_stg_grp
[factor] |
1. Stage_I.1
2. Stage_IIA.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Unknown.99 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_path_stg_grp
[factor] |
1. Stage_I.1
2. Stage_IIA.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Unknown.99 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_path_stg_grp
[factor] |
1. Stage_I.1
2. Stage_IIA.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SEER PATH STG GRP
Description: This data item is needed to store the results of the derived algorithmic calculation of Derived SEER Clinical Stage Group.
Rationale: The SEER Program is developing an algorithm to calculate clinical and pathologic stage group based on their T, N, and M components and additional information as needed to calculate stage. For example, for thyroid, additional information is needed on histology and age to calculate stage. Once the T, N, and M are known an algorithm can assign the stage group instead of a registrar having to look up the stage. There are also provisions for a separate field for directly assigned stage group if the registrar prefers entering it.
Codes
- 0 Stage 0
- 0A Stage 0A
- 01S Stage 0is
- 1 Stage I
- 1A Stage IA
- 1A1 Stage IA1
- 1A2 Stage IA2
- 1B Stage IB
- 1B1 Stage IB1
- 1C Stage IC
- 1S Stage IS
- 2 Stage 2
- 2A Stage 2A
- 2A1 Stage 2A1
- 2A2 Stage 2A2
- 2B Stage 2B
- 2C Stage 2C
- 3 Stage 3
- 3A Stage 3A
- 3B Stage 3B
- 3C Stage 3C
- 3C1 Stage 3C1
- 3C2 Stage 3C2
- 4 Stage 4
- 4A Stage 4A
- 4A1 Stage 4A1
- 4A2 Stage 4A2
- 4B Stage 4B
- 4C Stage 4C
- OC Stage OC
- 88 Not applicable
- 99 Unknown
- Blank The algorithm has not been run
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3610
All data
st_css() #IMPORTANT!
derivedseerclinstggrp <- as.factor(d[,"derivedseerclinstggrp"])
levels(derivedseerclinstggrp) <- list(Stage_I.1="1",
Stage_2A.2A="2A",
Stage_2B.2B="2B",
Stage_3.3="3",
Stage_4.4="4",
Unknown.99="99")
new.d <- data.frame(new.d, derivedseerclinstggrp)
new.d <- apply_labels(new.d, derivedseerclinstggrp = "derived_seer_clin_stg_grp")
#summary(new.d$derivedseerclinstggrp)
temp.d <- data.frame (new.d.1, derivedseerclinstggrp)
summarytools::view(dfSummary(new.d$derivedseerclinstggrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedseerclinstggrp
[labelled, factor] |
derived_seer_clin_stg_grp |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_2B.2B
4. Stage_3.3
5. Stage_4.4
6. Unknown.99 |
All NA's |
|
2167
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_clin_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_2B.2B
4. Stage_3.3
5. Stage_4.4
6. Unknown.99 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_clin_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_2B.2B
4. Stage_3.3
5. Stage_4.4
6. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_clin_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_2B.2B
4. Stage_3.3
5. Stage_4.4
6. Unknown.99 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_clin_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_2B.2B
4. Stage_3.3
5. Stage_4.4
6. Unknown.99 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_clin_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_2B.2B
4. Stage_3.3
5. Stage_4.4
6. Unknown.99 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_clin_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_2B.2B
4. Stage_3.3
5. Stage_4.4
6. Unknown.99 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_clin_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_2B.2B
4. Stage_3.3
5. Stage_4.4
6. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SEER CMB STG GRP
Description: This data item is needed to store the results of the derived algorithmic calculation of SEER Combined Stage Group.
Rationale: Rationale for change proposal (potential benefits of change): The SEER Program is developing an algorithm to calculate clinical and pathologic stage group based on their T, N, and M components and additional information as needed to calculate stage. For example, for thyroid, additional information is needed on histology and age to calculate stage. Once the T, N, and M are known an algorithm can assign the stage group instead of a registrar having to look up the stage. There are also provisions for a separate field for directly assigned stage group if the registrar prefers entering it.
Codes
- 0A Stage 0
- 0IS Stage 0is
- 1 Stage I
- 1A Stage IA
- 1A2 Stage IA2
- 1B Stage IB
- 1B1 Stage IB1
- 1B2 Stage IB2
- 1C Stage IC
- 1S Stage IS
- 2 Stage 2
- 2A Stage 2A
- 2A1 Stage
- 2A2 Stage IIA2
- 2B Stage IIB
- 2C Stage IIC
- 3 Stage III
- 3A Stage IIIA
- 3B Stage IIIB
- 3C Stage IIIC
- 3C1 Stage IIIC1
- 3C2 Stage IIIC2
- 4 Stage IV
- 4A Stage IVA
- 4A1 Stage IVA1
- 4A2 Stage IV42
- 4B Stage IVB
- 4C Stage IV4C
- OC Occult
- 88 Not applicable
- 99 Unknown
- Blank The algorithm has not been run
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3614
All data
st_css() #IMPORTANT!
derivedseercmbstggrp <- as.factor(trimws(d[,"derivedseercmbstggrp"]))
# THIS CODING NEEDS TO BE CONFIRMED
levels(derivedseercmbstggrp) <- list(Stage_I.1="1",
Stage_2A.2A="2A",
Stage_IIB.2B="2B",
Stage_III.3="3",
Stage_IV.4="4",
Not_applicable.88="88",
Unknown.99="99")
new.d <- data.frame(new.d, derivedseercmbstggrp)
new.d <- apply_labels(new.d, derivedseercmbstggrp = "Stage group based on their T, N, and M")
#summary(new.d$derivedseercmbstggrp)
temp.d <- data.frame (new.d.1, derivedseercmbstggrp)
summarytools::view(dfSummary(new.d$derivedseercmbstggrp, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedseercmbstggrp
[labelled, factor] |
Stage group based on their T, N, and M |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Not_applicable.88
7. Unknown.99 |
| 107 | ( | 21.2% | ) | | 65 | ( | 12.9% | ) | | 96 | ( | 19.0% | ) | | 110 | ( | 21.8% | ) | | 66 | ( | 13.1% | ) | | 0 | ( | 0.0% | ) | | 61 | ( | 12.1% | ) |
|
 |
1662
(76.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_cmb_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Not_applicable.88
7. Unknown.99 |
| 14 | ( | 35.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 11 | ( | 28.2% | ) | | 6 | ( | 15.4% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 20.5% | ) |
|
 |
150
(79.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_cmb_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Not_applicable.88
7. Unknown.99 |
| 15 | ( | 16.9% | ) | | 33 | ( | 37.1% | ) | | 19 | ( | 21.3% | ) | | 13 | ( | 14.6% | ) | | 7 | ( | 7.9% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.2% | ) |
|
 |
85
(48.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_cmb_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Not_applicable.88
7. Unknown.99 |
| 4 | ( | 4.1% | ) | | 9 | ( | 9.2% | ) | | 51 | ( | 52.0% | ) | | 17 | ( | 17.3% | ) | | 13 | ( | 13.3% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 4.1% | ) |
|
 |
139
(58.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_cmb_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Not_applicable.88
7. Unknown.99 |
| 10 | ( | 12.0% | ) | | 23 | ( | 27.7% | ) | | 26 | ( | 31.3% | ) | | 12 | ( | 14.5% | ) | | 9 | ( | 10.8% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.6% | ) |
|
 |
91
(52.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_cmb_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Not_applicable.88
7. Unknown.99 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_cmb_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Not_applicable.88
7. Unknown.99 |
| 64 | ( | 32.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 57 | ( | 29.1% | ) | | 31 | ( | 15.8% | ) | | 0 | ( | 0.0% | ) | | 44 | ( | 22.4% | ) |
|
 |
891
(82.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_cmb_stg_grp
[factor] |
1. Stage_I.1
2. Stage_2A.2A
3. Stage_IIB.2B
4. Stage_III.3
5. Stage_IV.4
6. Not_applicable.88
7. Unknown.99 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SEER COMBINED T
Description: This new data item is needed to store the results of the derived algorithmic calculation of Derived SEER Combined T.
Rationale: The SEER Program has collected data from 2004 on AJCC 6th T, N, M and stage and from 2010 on AJCC 7th T, N, M and stage based on algorithmic derivation from Collaborative Stage (CS) data. These data were based on combining information from both the clinical and pathologic into a combined (or ‘best’) derived T, N, M and stage group. SEER would like to continue to be able to derive a combined T, N, M and stage group in order to evaluate time trends in cancer incidence by stage. SEER is designing an algorithm to combine the clinical and pathologic information for T, N, and M into a derived combined T, N and M and then the combined T, N, and M and additional information as needed are used to derive a combined stage.
Codes (See the most recent versions of the AJCC Cancer Staging Manual and STORE manual)
- 88 Not applicable
- Blank Not derived
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3616
All data
st_css() #IMPORTANT!
derivedseercombinedt <- as.factor(d[,"derivedseercombinedt"])
new.d <- data.frame(new.d, derivedseercombinedt)
new.d <- apply_labels(new.d, derivedseercombinedt = "derived_seer_combined_t")
#summary(new.d$derivedseercombinedt)
temp.d <- data.frame (new.d.1, derivedseercombinedt)
summarytools::view(dfSummary(new.d$derivedseercombinedt, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedseercombinedt
[labelled, factor] |
derived_seer_combined_t |
1. c1 ·
2. c1A ·
3. c1C ·
4. c2 ·
5. c2A ·
6. c2B ·
7. c2C ·
8. c3 ·
9. c3A ·
10. c3B ·
11. c4 ·
12. cX ·
13. p2 ·
14. p2A ·
15. p2B ·
16. p2C ·
17. p3 ·
18. p3A ·
19. p3B ·
20. p4 · |
| 9 | ( | 1.0% | ) | | 2 | ( | 0.2% | ) | | 403 | ( | 44.8% | ) | | 17 | ( | 1.9% | ) | | 26 | ( | 2.9% | ) | | 16 | ( | 1.8% | ) | | 34 | ( | 3.8% | ) | | 1 | ( | 0.1% | ) | | 9 | ( | 1.0% | ) | | 8 | ( | 0.9% | ) | | 1 | ( | 0.1% | ) | | 9 | ( | 1.0% | ) | | 16 | ( | 1.8% | ) | | 15 | ( | 1.7% | ) | | 4 | ( | 0.4% | ) | | 211 | ( | 23.4% | ) | | 1 | ( | 0.1% | ) | | 69 | ( | 7.7% | ) | | 48 | ( | 5.3% | ) | | 1 | ( | 0.1% | ) |
|
 |
1267
(58.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_t
[factor] |
1. c1 ·
2. c1A ·
3. c1C ·
4. c2 ·
5. c2A ·
6. c2B ·
7. c2C ·
8. c3 ·
9. c3A ·
10. c3B ·
11. c4 ·
12. cX ·
13. p2 ·
14. p2A ·
15. p2B ·
16. p2C ·
17. p3 ·
18. p3A ·
19. p3B ·
20. p4 · |
| 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 40 | ( | 41.7% | ) | | 1 | ( | 1.0% | ) | | 2 | ( | 2.1% | ) | | 2 | ( | 2.1% | ) | | 6 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.1% | ) | | 1 | ( | 1.0% | ) | | 2 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 24 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 6.2% | ) | | 5 | ( | 5.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
93
(49.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_t
[factor] |
1. c1 ·
2. c1A ·
3. c1C ·
4. c2 ·
5. c2A ·
6. c2B ·
7. c2C ·
8. c3 ·
9. c3A ·
10. c3B ·
11. c4 ·
12. cX ·
13. p2 ·
14. p2A ·
15. p2B ·
16. p2C ·
17. p3 ·
18. p3A ·
19. p3B ·
20. p4 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 44 | ( | 51.2% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 10.5% | ) | | 1 | ( | 1.2% | ) | | 6 | ( | 7.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.5% | ) | | 0 | ( | 0.0% | ) | | 7 | ( | 8.1% | ) | | 0 | ( | 0.0% | ) | | 10 | ( | 11.6% | ) | | 3 | ( | 3.5% | ) | | 0 | ( | 0.0% | ) |
|
 |
88
(50.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_t
[factor] |
1. c1 ·
2. c1A ·
3. c1C ·
4. c2 ·
5. c2A ·
6. c2B ·
7. c2C ·
8. c3 ·
9. c3A ·
10. c3B ·
11. c4 ·
12. cX ·
13. p2 ·
14. p2A ·
15. p2B ·
16. p2C ·
17. p3 ·
18. p3A ·
19. p3B ·
20. p4 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 17 | ( | 17.7% | ) | | 4 | ( | 4.2% | ) | | 3 | ( | 3.1% | ) | | 1 | ( | 1.0% | ) | | 6 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 42 | ( | 43.8% | ) | | 1 | ( | 1.0% | ) | | 14 | ( | 14.6% | ) | | 4 | ( | 4.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
141
(59.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_t
[factor] |
1. c1 ·
2. c1A ·
3. c1C ·
4. c2 ·
5. c2A ·
6. c2B ·
7. c2C ·
8. c3 ·
9. c3A ·
10. c3B ·
11. c4 ·
12. cX ·
13. p2 ·
14. p2A ·
15. p2B ·
16. p2C ·
17. p3 ·
18. p3A ·
19. p3B ·
20. p4 · |
| 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 33 | ( | 39.8% | ) | | 5 | ( | 6.0% | ) | | 2 | ( | 2.4% | ) | | 2 | ( | 2.4% | ) | | 2 | ( | 2.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.4% | ) | | 2 | ( | 2.4% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 15 | ( | 18.1% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 10.8% | ) | | 8 | ( | 9.6% | ) | | 0 | ( | 0.0% | ) |
|
 |
91
(52.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_t
[factor] |
1. c1 ·
2. c1A ·
3. c1C ·
4. c2 ·
5. c2A ·
6. c2B ·
7. c2C ·
8. c3 ·
9. c3A ·
10. c3B ·
11. c4 ·
12. cX ·
13. p2 ·
14. p2A ·
15. p2B ·
16. p2C ·
17. p3 ·
18. p3A ·
19. p3B ·
20. p4 · |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_t
[factor] |
1. c1 ·
2. c1A ·
3. c1C ·
4. c2 ·
5. c2A ·
6. c2B ·
7. c2C ·
8. c3 ·
9. c3A ·
10. c3B ·
11. c4 ·
12. cX ·
13. p2 ·
14. p2A ·
15. p2B ·
16. p2C ·
17. p3 ·
18. p3A ·
19. p3B ·
20. p4 · |
| 8 | ( | 1.5% | ) | | 1 | ( | 0.2% | ) | | 269 | ( | 49.9% | ) | | 7 | ( | 1.3% | ) | | 10 | ( | 1.9% | ) | | 10 | ( | 1.9% | ) | | 14 | ( | 2.6% | ) | | 1 | ( | 0.2% | ) | | 6 | ( | 1.1% | ) | | 4 | ( | 0.7% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 0.4% | ) | | 13 | ( | 2.4% | ) | | 8 | ( | 1.5% | ) | | 4 | ( | 0.7% | ) | | 123 | ( | 22.8% | ) | | 0 | ( | 0.0% | ) | | 30 | ( | 5.6% | ) | | 28 | ( | 5.2% | ) | | 1 | ( | 0.2% | ) |
|
 |
548
(50.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_t
[factor] |
1. c1 ·
2. c1A ·
3. c1C ·
4. c2 ·
5. c2A ·
6. c2B ·
7. c2C ·
8. c3 ·
9. c3A ·
10. c3B ·
11. c4 ·
12. cX ·
13. p2 ·
14. p2A ·
15. p2B ·
16. p2C ·
17. p3 ·
18. p3A ·
19. p3B ·
20. p4 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SEER COMBINED N
Description: This item is used to store the results of the source information selected for the derived algorithmic calculation of Combined T, N, and M.
Rationale: The SEER Program has collected data from 2004 on AJCC 6th T, N, M and stage and from 2010 on AJCC 7th T, N, M and stage based on algorithmic derivation from Collaborative Stage (CS) data. These data were based on combining information from both the clinical and pathologic into a combined (or ‘best’) derived T, N, M and stage group. SEER would like to continue to be able to derive a combined T, N, M and stage group in order to evaluate time trends in cancer incidence by stage. SEER is designing an algorithm to combine the clinical and pathologic information for T, N, and M into a derived combined T, N and M and then the combined T, N, and M and additional information as needed are used to derive a combined stage. These derived combined T, N, M and stage items need to be new data items.
Codes (See the most recent versions of the AJCC Cancer Staging Manual and STORE manual)
- 88 Not applicable
- Blank Not derived
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3618
All data
st_css() #IMPORTANT!
derivedseercombinedn <- as.factor(d[,"derivedseercombinedn"])
new.d <- data.frame(new.d, derivedseercombinedn)
new.d <- apply_labels(new.d, derivedseercombinedn = "derived_seer_combined_n")
#summary(new.d$derivedseercombinedn)
temp.d <- data.frame (new.d.1, derivedseercombinedn)
summarytools::view(dfSummary(new.d$derivedseercombinedn, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedseercombinedn
[labelled, factor] |
derived_seer_combined_n |
1. c0 ·
2. c1 ·
3. cX ·
4. p0 ·
5. p1 ·
6. pX · |
| 608 | ( | 67.6% | ) | | 22 | ( | 2.4% | ) | | 22 | ( | 2.4% | ) | | 219 | ( | 24.3% | ) | | 27 | ( | 3.0% | ) | | 2 | ( | 0.2% | ) |
|
 |
1267
(58.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_n
[factor] |
1. c0 ·
2. c1 ·
3. cX ·
4. p0 ·
5. p1 ·
6. pX · |
| 70 | ( | 72.9% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.1% | ) | | 19 | ( | 19.8% | ) | | 3 | ( | 3.1% | ) | | 1 | ( | 1.0% | ) |
|
 |
93
(49.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_n
[factor] |
1. c0 ·
2. c1 ·
3. cX ·
4. p0 ·
5. p1 ·
6. pX · |
| 61 | ( | 70.9% | ) | | 5 | ( | 5.8% | ) | | 1 | ( | 1.2% | ) | | 18 | ( | 20.9% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
88
(50.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_n
[factor] |
1. c0 ·
2. c1 ·
3. cX ·
4. p0 ·
5. p1 ·
6. pX · |
| 34 | ( | 35.4% | ) | | 9 | ( | 9.4% | ) | | 2 | ( | 2.1% | ) | | 48 | ( | 50.0% | ) | | 3 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) |
|
 |
141
(59.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_n
[factor] |
1. c0 ·
2. c1 ·
3. cX ·
4. p0 ·
5. p1 ·
6. pX · |
| 51 | ( | 61.4% | ) | | 1 | ( | 1.2% | ) | | 2 | ( | 2.4% | ) | | 23 | ( | 27.7% | ) | | 6 | ( | 7.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
91
(52.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_n
[factor] |
1. c0 ·
2. c1 ·
3. cX ·
4. p0 ·
5. p1 ·
6. pX · |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_n
[factor] |
1. c0 ·
2. c1 ·
3. cX ·
4. p0 ·
5. p1 ·
6. pX · |
| 392 | ( | 72.7% | ) | | 7 | ( | 1.3% | ) | | 14 | ( | 2.6% | ) | | 111 | ( | 20.6% | ) | | 14 | ( | 2.6% | ) | | 1 | ( | 0.2% | ) |
|
 |
548
(50.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_n
[factor] |
1. c0 ·
2. c1 ·
3. cX ·
4. p0 ·
5. p1 ·
6. pX · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
DERIVED SEER COMBINED M
Description: This item is used to store the results of the source information selected for the derived algorithmic calculation of Combined T, N, and M.
Rationale: The SEER Program has collected data from 2004 on AJCC 6th T, N, M and stage and from 2010 on AJCC 7th T, N, M and stage based on algorithmic derivation from Collaborative Stage (CS) data. These data were based on combining information from both the clinical and pathologic into a combined (or ‘best’) derived T, N, M and stage group. SEER would like to continue to be able to derive a combined T, N, M and stage group in order to evaluate time trends in cancer incidence by stage. SEER is designing an algorithm to combine the clinical and pathologic information for T, N, and M into a derived combined T, N and M and then the combined T, N, and M and additional information as needed are used to derive a combined stage. These derived combined T, N, M and stage items need to be new data items.
Codes (See the most recent versions of the AJCC Cancer Staging Manual and STORE manual)
- 88 Not applicable
- Blank Not derived
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3620
All data
st_css() #IMPORTANT!
derivedseercombinedm <- as.factor(d[,"derivedseercombinedm"])
new.d <- data.frame(new.d, derivedseercombinedm)
new.d <- apply_labels(new.d, derivedseercombinedm = "derived_seer_combined_m")
#summary(new.d$derivedseercombinedm)
temp.d <- data.frame (new.d.1, derivedseercombinedm)
summarytools::view(dfSummary(new.d$derivedseercombinedm, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derivedseercombinedm
[labelled, factor] |
derived_seer_combined_m |
1. c0 ·
2. c1 ·
3. c1A ·
4. c1B ·
5. c1C ·
6. p1A ·
7. p1C · |
| 867 | ( | 96.3% | ) | | 4 | ( | 0.4% | ) | | 3 | ( | 0.3% | ) | | 20 | ( | 2.2% | ) | | 3 | ( | 0.3% | ) | | 2 | ( | 0.2% | ) | | 1 | ( | 0.1% | ) |
|
 |
1267
(58.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_m
[factor] |
1. c0 ·
2. c1 ·
3. c1A ·
4. c1B ·
5. c1C ·
6. p1A ·
7. p1C · |
| 93 | ( | 96.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
93
(49.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_m
[factor] |
1. c0 ·
2. c1 ·
3. c1A ·
4. c1B ·
5. c1C ·
6. p1A ·
7. p1C · |
| 82 | ( | 95.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.5% | ) | | 1 | ( | 1.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
88
(50.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_m
[factor] |
1. c0 ·
2. c1 ·
3. c1A ·
4. c1B ·
5. c1C ·
6. p1A ·
7. p1C · |
| 89 | ( | 92.7% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 2 | ( | 2.1% | ) | | 2 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) |
|
 |
141
(59.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_m
[factor] |
1. c0 ·
2. c1 ·
3. c1A ·
4. c1B ·
5. c1C ·
6. p1A ·
7. p1C · |
| 80 | ( | 96.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
91
(52.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_m
[factor] |
1. c0 ·
2. c1 ·
3. c1A ·
4. c1B ·
5. c1C ·
6. p1A ·
7. p1C · |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_m
[factor] |
1. c0 ·
2. c1 ·
3. c1A ·
4. c1B ·
5. c1C ·
6. p1A ·
7. p1C · |
| 523 | ( | 97.0% | ) | | 3 | ( | 0.6% | ) | | 2 | ( | 0.4% | ) | | 10 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.2% | ) | | 0 | ( | 0.0% | ) |
|
 |
548
(50.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
derived_seer_combined_m
[factor] |
1. c0 ·
2. c1 ·
3. c1A ·
4. c1B ·
5. c1C ·
6. p1A ·
7. p1C · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 1
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes (See the most recent versions of the AJCC Cancer Staging Manual and STORE manual)
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3780
All data
st_css() #IMPORTANT!
secondarydiagnosis1 <- as.factor(d[,"secondarydiagnosis1"])
new.d <- data.frame(new.d, secondarydiagnosis1)
new.d <- apply_labels(new.d, secondarydiagnosis1 = "secondary_diagnosis_1")
#summary(new.d$secondarydiagnosis1)
temp.d <- data.frame (new.d.1, secondarydiagnosis1)
summarytools::view(dfSummary(new.d$secondarydiagnosis1, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis1
[labelled, factor] |
secondary_diagnosis_1 |
1. 0 · |
|
 |
1447
(66.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_1
[factor] |
1. 0 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_1
[factor] |
1. 0 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_1
[factor] |
1. 0 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_1
[factor] |
1. 0 · |
|
 |
122
(70.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_1
[factor] |
1. 0 · |
|
 |
215
(74.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_1
[factor] |
1. 0 · |
|
 |
498
(45.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_1
[factor] |
1. 0 · |
|
 |
12
(75.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 2
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3782
All data
st_css() #IMPORTANT!
secondarydiagnosis2 <- as.factor(d[,"secondarydiagnosis2"])
new.d <- data.frame(new.d, secondarydiagnosis2)
new.d <- apply_labels(new.d, secondarydiagnosis2 = "secondary_diagnosis_2")
#summary(new.d$secondarydiagnosis2)
temp.d <- data.frame (new.d.1, secondarydiagnosis2)
summarytools::view(dfSummary(new.d$secondarydiagnosis2, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis2
[labelled, factor] |
secondary_diagnosis_2 |
1. A419 ·
2. B182000
3. B20 ·
4. E039 ·
5. E081000
6. E1122 ·
7. E1165 ·
8. E116500
9. E1169 ·
10. E118 ·
11. E119 ·
12. E119000
13. E669 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E782000
18. E785 ·
19. E785000
20. E835200
21. E876 ·
22. G40909 ·
23. G4733 ·
24. G560 ·
25. H40 ·
26. H409 ·
27. I10 ·
28. I1000 ·
29. I100000
30. I119 ·
31. I120 ·
32. I129 ·
33. I251 ·
34. I2510 ·
35. I251000
36. I252 ·
37. I341 ·
38. I5022 ·
39. I5042 ·
40. I509 ·
41. I700 ·
42. I739 ·
43. I82509 ·
44. I878 ·
45. J302 ·
46. J449 ·
47. J45 ·
48. J4590 ·
49. J45909 ·
50. J459090
[ 64 others ] |
| 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 20 | ( | 6.8% | ) | | 1 | ( | 0.3% | ) | | 4 | ( | 1.4% | ) | | 7 | ( | 2.4% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 40 | ( | 13.6% | ) | | 6 | ( | 2.0% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 3 | ( | 1.0% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 38 | ( | 12.9% | ) | | 1 | ( | 0.3% | ) | | 4 | ( | 1.4% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 2 | ( | 0.7% | ) | | 1 | ( | 0.3% | ) | | 5 | ( | 1.7% | ) | | 1 | ( | 0.3% | ) | | 1 | ( | 0.3% | ) | | 3 | ( | 1.0% | ) | | 1 | ( | 0.3% | ) | | 114 | ( | 38.8% | ) |
|
 |
1873
(86.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_2
[factor] |
1. A419 ·
2. B182000
3. B20 ·
4. E039 ·
5. E081000
6. E1122 ·
7. E1165 ·
8. E116500
9. E1169 ·
10. E118 ·
11. E119 ·
12. E119000
13. E669 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E782000
18. E785 ·
19. E785000
20. E835200
21. E876 ·
22. G40909 ·
23. G4733 ·
24. G560 ·
25. H40 ·
26. H409 ·
27. I10 ·
28. I1000 ·
29. I100000
30. I119 ·
31. I120 ·
32. I129 ·
33. I251 ·
34. I2510 ·
35. I251000
36. I252 ·
37. I341 ·
38. I5022 ·
39. I5042 ·
40. I509 ·
41. I700 ·
42. I739 ·
43. I82509 ·
44. I878 ·
45. J302 ·
46. J449 ·
47. J45 ·
48. J4590 ·
49. J45909 ·
50. J459090
51. J479 ·
52. J841000
53. J954 ·
54. J984 ·
55. K219 ·
56. K409000
57. K429 ·
58. K429000
59. K611 ·
60. K660 ·
61. K7460 ·
62. K768900
63. K802000
64. K862 ·
65. K913 ·
66. L732000
67. M069 ·
68. M109 ·
69. M129 ·
70. M15 ·
71. M160 ·
72. M1990 ·
73. M513600
74. N138 ·
75. N139 ·
76. N186 ·
77. N189 ·
78. N281 ·
79. N289 ·
80. N390 ·
81. N393 ·
82. N3941 ·
83. N3943 ·
84. N40 ·
85. N40.0 ·
86. N400 ·
87. N40000 ·
88. N401 ·
89. N4010 ·
90. N4110 ·
91. N5201 ·
92. N5203 ·
93. N5231 ·
94. N528 ·
95. N529 ·
96. R001 ·
97. R030 ·
98. R060000
99. R1030 ·
100. R160000
101. R312 ·
102. R319 ·
103. R350 ·
104. R351 ·
105. R3510 ·
106. R3915 ·
107. R51 ·
108. R5383 ·
109. R590 ·
110. R972 ·
111. R9720 ·
112. Z8042 ·
113. Z86010 ·
114. Z87891 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_2
[factor] |
1. A419 ·
2. B182000
3. B20 ·
4. E039 ·
5. E081000
6. E1122 ·
7. E1165 ·
8. E116500
9. E1169 ·
10. E118 ·
11. E119 ·
12. E119000
13. E669 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E782000
18. E785 ·
19. E785000
20. E835200
21. E876 ·
22. G40909 ·
23. G4733 ·
24. G560 ·
25. H40 ·
26. H409 ·
27. I10 ·
28. I1000 ·
29. I100000
30. I119 ·
31. I120 ·
32. I129 ·
33. I251 ·
34. I2510 ·
35. I251000
36. I252 ·
37. I341 ·
38. I5022 ·
39. I5042 ·
40. I509 ·
41. I700 ·
42. I739 ·
43. I82509 ·
44. I878 ·
45. J302 ·
46. J449 ·
47. J45 ·
48. J4590 ·
49. J45909 ·
50. J459090
51. J479 ·
52. J841000
53. J954 ·
54. J984 ·
55. K219 ·
56. K409000
57. K429 ·
58. K429000
59. K611 ·
60. K660 ·
61. K7460 ·
62. K768900
63. K802000
64. K862 ·
65. K913 ·
66. L732000
67. M069 ·
68. M109 ·
69. M129 ·
70. M15 ·
71. M160 ·
72. M1990 ·
73. M513600
74. N138 ·
75. N139 ·
76. N186 ·
77. N189 ·
78. N281 ·
79. N289 ·
80. N390 ·
81. N393 ·
82. N3941 ·
83. N3943 ·
84. N40 ·
85. N40.0 ·
86. N400 ·
87. N40000 ·
88. N401 ·
89. N4010 ·
90. N4110 ·
91. N5201 ·
92. N5203 ·
93. N5231 ·
94. N528 ·
95. N529 ·
96. R001 ·
97. R030 ·
98. R060000
99. R1030 ·
100. R160000
101. R312 ·
102. R319 ·
103. R350 ·
104. R351 ·
105. R3510 ·
106. R3915 ·
107. R51 ·
108. R5383 ·
109. R590 ·
110. R972 ·
111. R9720 ·
112. Z8042 ·
113. Z86010 ·
114. Z87891 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_2
[factor] |
1. A419 ·
2. B182000
3. B20 ·
4. E039 ·
5. E081000
6. E1122 ·
7. E1165 ·
8. E116500
9. E1169 ·
10. E118 ·
11. E119 ·
12. E119000
13. E669 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E782000
18. E785 ·
19. E785000
20. E835200
21. E876 ·
22. G40909 ·
23. G4733 ·
24. G560 ·
25. H40 ·
26. H409 ·
27. I10 ·
28. I1000 ·
29. I100000
30. I119 ·
31. I120 ·
32. I129 ·
33. I251 ·
34. I2510 ·
35. I251000
36. I252 ·
37. I341 ·
38. I5022 ·
39. I5042 ·
40. I509 ·
41. I700 ·
42. I739 ·
43. I82509 ·
44. I878 ·
45. J302 ·
46. J449 ·
47. J45 ·
48. J4590 ·
49. J45909 ·
50. J459090
51. J479 ·
52. J841000
53. J954 ·
54. J984 ·
55. K219 ·
56. K409000
57. K429 ·
58. K429000
59. K611 ·
60. K660 ·
61. K7460 ·
62. K768900
63. K802000
64. K862 ·
65. K913 ·
66. L732000
67. M069 ·
68. M109 ·
69. M129 ·
70. M15 ·
71. M160 ·
72. M1990 ·
73. M513600
74. N138 ·
75. N139 ·
76. N186 ·
77. N189 ·
78. N281 ·
79. N289 ·
80. N390 ·
81. N393 ·
82. N3941 ·
83. N3943 ·
84. N40 ·
85. N40.0 ·
86. N400 ·
87. N40000 ·
88. N401 ·
89. N4010 ·
90. N4110 ·
91. N5201 ·
92. N5203 ·
93. N5231 ·
94. N528 ·
95. N529 ·
96. R001 ·
97. R030 ·
98. R060000
99. R1030 ·
100. R160000
101. R312 ·
102. R319 ·
103. R350 ·
104. R351 ·
105. R3510 ·
106. R3915 ·
107. R51 ·
108. R5383 ·
109. R590 ·
110. R972 ·
111. R9720 ·
112. Z8042 ·
113. Z86010 ·
114. Z87891 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_2
[factor] |
1. A419 ·
2. B182000
3. B20 ·
4. E039 ·
5. E081000
6. E1122 ·
7. E1165 ·
8. E116500
9. E1169 ·
10. E118 ·
11. E119 ·
12. E119000
13. E669 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E782000
18. E785 ·
19. E785000
20. E835200
21. E876 ·
22. G40909 ·
23. G4733 ·
24. G560 ·
25. H40 ·
26. H409 ·
27. I10 ·
28. I1000 ·
29. I100000
30. I119 ·
31. I120 ·
32. I129 ·
33. I251 ·
34. I2510 ·
35. I251000
36. I252 ·
37. I341 ·
38. I5022 ·
39. I5042 ·
40. I509 ·
41. I700 ·
42. I739 ·
43. I82509 ·
44. I878 ·
45. J302 ·
46. J449 ·
47. J45 ·
48. J4590 ·
49. J45909 ·
50. J459090
[ 64 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 10.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 2 | ( | 4.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 8.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 4.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 6.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 4.3% | ) | | 0 | ( | 0.0% | ) | | 19 | ( | 41.3% | ) |
|
 |
128
(73.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_2
[factor] |
1. A419 ·
2. B182000
3. B20 ·
4. E039 ·
5. E081000
6. E1122 ·
7. E1165 ·
8. E116500
9. E1169 ·
10. E118 ·
11. E119 ·
12. E119000
13. E669 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E782000
18. E785 ·
19. E785000
20. E835200
21. E876 ·
22. G40909 ·
23. G4733 ·
24. G560 ·
25. H40 ·
26. H409 ·
27. I10 ·
28. I1000 ·
29. I100000
30. I119 ·
31. I120 ·
32. I129 ·
33. I251 ·
34. I2510 ·
35. I251000
36. I252 ·
37. I341 ·
38. I5022 ·
39. I5042 ·
40. I509 ·
41. I700 ·
42. I739 ·
43. I82509 ·
44. I878 ·
45. J302 ·
46. J449 ·
47. J45 ·
48. J4590 ·
49. J45909 ·
50. J459090
[ 64 others ] |
| 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.1% | ) | | 6 | ( | 6.4% | ) | | 1 | ( | 1.1% | ) | | 2 | ( | 2.1% | ) | | 2 | ( | 2.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 16 | ( | 17.0% | ) | | 3 | ( | 3.2% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 16 | ( | 17.0% | ) | | 1 | ( | 1.1% | ) | | 3 | ( | 3.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 29 | ( | 30.9% | ) |
|
 |
196
(67.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_2
[factor] |
1. A419 ·
2. B182000
3. B20 ·
4. E039 ·
5. E081000
6. E1122 ·
7. E1165 ·
8. E116500
9. E1169 ·
10. E118 ·
11. E119 ·
12. E119000
13. E669 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E782000
18. E785 ·
19. E785000
20. E835200
21. E876 ·
22. G40909 ·
23. G4733 ·
24. G560 ·
25. H40 ·
26. H409 ·
27. I10 ·
28. I1000 ·
29. I100000
30. I119 ·
31. I120 ·
32. I129 ·
33. I251 ·
34. I2510 ·
35. I251000
36. I252 ·
37. I341 ·
38. I5022 ·
39. I5042 ·
40. I509 ·
41. I700 ·
42. I739 ·
43. I82509 ·
44. I878 ·
45. J302 ·
46. J449 ·
47. J45 ·
48. J4590 ·
49. J45909 ·
50. J459090
[ 64 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 9 | ( | 5.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 3 | ( | 1.9% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 20 | ( | 13.0% | ) | | 3 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 2 | ( | 1.3% | ) | | 19 | ( | 12.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 0.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 1.3% | ) | | 1 | ( | 0.6% | ) | | 4 | ( | 2.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 1 | ( | 0.6% | ) | | 66 | ( | 42.9% | ) |
|
 |
933
(85.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_2
[factor] |
1. A419 ·
2. B182000
3. B20 ·
4. E039 ·
5. E081000
6. E1122 ·
7. E1165 ·
8. E116500
9. E1169 ·
10. E118 ·
11. E119 ·
12. E119000
13. E669 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E782000
18. E785 ·
19. E785000
20. E835200
21. E876 ·
22. G40909 ·
23. G4733 ·
24. G560 ·
25. H40 ·
26. H409 ·
27. I10 ·
28. I1000 ·
29. I100000
30. I119 ·
31. I120 ·
32. I129 ·
33. I251 ·
34. I2510 ·
35. I251000
36. I252 ·
37. I341 ·
38. I5022 ·
39. I5042 ·
40. I509 ·
41. I700 ·
42. I739 ·
43. I82509 ·
44. I878 ·
45. J302 ·
46. J449 ·
47. J45 ·
48. J4590 ·
49. J45909 ·
50. J459090
51. J479 ·
52. J841000
53. J954 ·
54. J984 ·
55. K219 ·
56. K409000
57. K429 ·
58. K429000
59. K611 ·
60. K660 ·
61. K7460 ·
62. K768900
63. K802000
64. K862 ·
65. K913 ·
66. L732000
67. M069 ·
68. M109 ·
69. M129 ·
70. M15 ·
71. M160 ·
72. M1990 ·
73. M513600
74. N138 ·
75. N139 ·
76. N186 ·
77. N189 ·
78. N281 ·
79. N289 ·
80. N390 ·
81. N393 ·
82. N3941 ·
83. N3943 ·
84. N40 ·
85. N40.0 ·
86. N400 ·
87. N40000 ·
88. N401 ·
89. N4010 ·
90. N4110 ·
91. N5201 ·
92. N5203 ·
93. N5231 ·
94. N528 ·
95. N529 ·
96. R001 ·
97. R030 ·
98. R060000
99. R1030 ·
100. R160000
101. R312 ·
102. R319 ·
103. R350 ·
104. R351 ·
105. R3510 ·
106. R3915 ·
107. R51 ·
108. R5383 ·
109. R590 ·
110. R972 ·
111. R9720 ·
112. Z8042 ·
113. Z86010 ·
114. Z87891 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 3
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3784
All data
st_css() #IMPORTANT!
secondarydiagnosis3 <- as.factor(d[,"secondarydiagnosis3"])
new.d <- data.frame(new.d, secondarydiagnosis3)
new.d <- apply_labels(new.d, secondarydiagnosis3 = "secondary_diagnosis_3")
#summary(new.d$secondarydiagnosis3)
temp.d <- data.frame (new.d.1, secondarydiagnosis3)
summarytools::view(dfSummary(new.d$secondarydiagnosis3, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis3
[labelled, factor] |
secondary_diagnosis_3 |
1. B1710 ·
2. B182 ·
3. E039 ·
4. E039000
5. E1169 ·
6. E119 ·
7. E119000
8. E291 ·
9. E6601 ·
10. E663 ·
11. E669 ·
12. E669000
13. E78 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E784 ·
18. E785 ·
19. E785000
20. E875 ·
21. E876 ·
22. G4730 ·
23. G4733 ·
24. G609000
25. G8929 ·
26. H409 ·
27. I10 ·
28. I100000
29. I1129 ·
30. I129 ·
31. I2510 ·
32. I495 ·
33. I50 ·
34. I509 ·
35. I700000
36. I709000
37. I739 ·
38. I7789 ·
39. I82401 ·
40. I898 ·
41. I959 ·
42. J302 ·
43. J449 ·
44. J45 ·
45. J984 ·
46. K219 ·
47. K4020 ·
48. K4090 ·
49. K429000
50. K579000
[ 47 others ] |
| 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 11 | ( | 5.9% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 5 | ( | 2.7% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 17 | ( | 9.1% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 4 | ( | 2.1% | ) | | 19 | ( | 10.2% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 3 | ( | 1.6% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 2 | ( | 1.1% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 9 | ( | 4.8% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 1 | ( | 0.5% | ) | | 73 | ( | 39.0% | ) |
|
 |
1980
(91.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_3
[factor] |
1. B1710 ·
2. B182 ·
3. E039 ·
4. E039000
5. E1169 ·
6. E119 ·
7. E119000
8. E291 ·
9. E6601 ·
10. E663 ·
11. E669 ·
12. E669000
13. E78 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E784 ·
18. E785 ·
19. E785000
20. E875 ·
21. E876 ·
22. G4730 ·
23. G4733 ·
24. G609000
25. G8929 ·
26. H409 ·
27. I10 ·
28. I100000
29. I1129 ·
30. I129 ·
31. I2510 ·
32. I495 ·
33. I50 ·
34. I509 ·
35. I700000
36. I709000
37. I739 ·
38. I7789 ·
39. I82401 ·
40. I898 ·
41. I959 ·
42. J302 ·
43. J449 ·
44. J45 ·
45. J984 ·
46. K219 ·
47. K4020 ·
48. K4090 ·
49. K429000
50. K579000
51. K5900 ·
52. K603 ·
53. L409 ·
54. M10 ·
55. M109 ·
56. M1990 ·
57. M199000
58. M47815 ·
59. M478160
60. M549 ·
61. N179 ·
62. N183 ·
63. N189 ·
64. N281 ·
65. N318 ·
66. N3941 ·
67. N40 ·
68. N400 ·
69. N401 ·
70. N4289 ·
71. N432 ·
72. N5201 ·
73. N529 ·
74. N9971 ·
75. N99842 ·
76. R05 ·
77. R200 ·
78. R3100 ·
79. R338 ·
80. R339 ·
81. R351 ·
82. R3915 ·
83. R5383 ·
84. R590 ·
85. R631 ·
86. R9431 ·
87. R972 ·
88. R9720 ·
89. Z6831 ·
90. Z801 ·
91. Z8546 ·
92. Z8673 ·
93. Z87891 ·
94. Z880 ·
95. Z955000
96. Z98890 ·
97. Z992 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_3
[factor] |
1. B1710 ·
2. B182 ·
3. E039 ·
4. E039000
5. E1169 ·
6. E119 ·
7. E119000
8. E291 ·
9. E6601 ·
10. E663 ·
11. E669 ·
12. E669000
13. E78 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E784 ·
18. E785 ·
19. E785000
20. E875 ·
21. E876 ·
22. G4730 ·
23. G4733 ·
24. G609000
25. G8929 ·
26. H409 ·
27. I10 ·
28. I100000
29. I1129 ·
30. I129 ·
31. I2510 ·
32. I495 ·
33. I50 ·
34. I509 ·
35. I700000
36. I709000
37. I739 ·
38. I7789 ·
39. I82401 ·
40. I898 ·
41. I959 ·
42. J302 ·
43. J449 ·
44. J45 ·
45. J984 ·
46. K219 ·
47. K4020 ·
48. K4090 ·
49. K429000
50. K579000
51. K5900 ·
52. K603 ·
53. L409 ·
54. M10 ·
55. M109 ·
56. M1990 ·
57. M199000
58. M47815 ·
59. M478160
60. M549 ·
61. N179 ·
62. N183 ·
63. N189 ·
64. N281 ·
65. N318 ·
66. N3941 ·
67. N40 ·
68. N400 ·
69. N401 ·
70. N4289 ·
71. N432 ·
72. N5201 ·
73. N529 ·
74. N9971 ·
75. N99842 ·
76. R05 ·
77. R200 ·
78. R3100 ·
79. R338 ·
80. R339 ·
81. R351 ·
82. R3915 ·
83. R5383 ·
84. R590 ·
85. R631 ·
86. R9431 ·
87. R972 ·
88. R9720 ·
89. Z6831 ·
90. Z801 ·
91. Z8546 ·
92. Z8673 ·
93. Z87891 ·
94. Z880 ·
95. Z955000
96. Z98890 ·
97. Z992 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_3
[factor] |
1. B1710 ·
2. B182 ·
3. E039 ·
4. E039000
5. E1169 ·
6. E119 ·
7. E119000
8. E291 ·
9. E6601 ·
10. E663 ·
11. E669 ·
12. E669000
13. E78 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E784 ·
18. E785 ·
19. E785000
20. E875 ·
21. E876 ·
22. G4730 ·
23. G4733 ·
24. G609000
25. G8929 ·
26. H409 ·
27. I10 ·
28. I100000
29. I1129 ·
30. I129 ·
31. I2510 ·
32. I495 ·
33. I50 ·
34. I509 ·
35. I700000
36. I709000
37. I739 ·
38. I7789 ·
39. I82401 ·
40. I898 ·
41. I959 ·
42. J302 ·
43. J449 ·
44. J45 ·
45. J984 ·
46. K219 ·
47. K4020 ·
48. K4090 ·
49. K429000
50. K579000
51. K5900 ·
52. K603 ·
53. L409 ·
54. M10 ·
55. M109 ·
56. M1990 ·
57. M199000
58. M47815 ·
59. M478160
60. M549 ·
61. N179 ·
62. N183 ·
63. N189 ·
64. N281 ·
65. N318 ·
66. N3941 ·
67. N40 ·
68. N400 ·
69. N401 ·
70. N4289 ·
71. N432 ·
72. N5201 ·
73. N529 ·
74. N9971 ·
75. N99842 ·
76. R05 ·
77. R200 ·
78. R3100 ·
79. R338 ·
80. R339 ·
81. R351 ·
82. R3915 ·
83. R5383 ·
84. R590 ·
85. R631 ·
86. R9431 ·
87. R972 ·
88. R9720 ·
89. Z6831 ·
90. Z801 ·
91. Z8546 ·
92. Z8673 ·
93. Z87891 ·
94. Z880 ·
95. Z955000
96. Z98890 ·
97. Z992 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_3
[factor] |
1. B1710 ·
2. B182 ·
3. E039 ·
4. E039000
5. E1169 ·
6. E119 ·
7. E119000
8. E291 ·
9. E6601 ·
10. E663 ·
11. E669 ·
12. E669000
13. E78 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E784 ·
18. E785 ·
19. E785000
20. E875 ·
21. E876 ·
22. G4730 ·
23. G4733 ·
24. G609000
25. G8929 ·
26. H409 ·
27. I10 ·
28. I100000
29. I1129 ·
30. I129 ·
31. I2510 ·
32. I495 ·
33. I50 ·
34. I509 ·
35. I700000
36. I709000
37. I739 ·
38. I7789 ·
39. I82401 ·
40. I898 ·
41. I959 ·
42. J302 ·
43. J449 ·
44. J45 ·
45. J984 ·
46. K219 ·
47. K4020 ·
48. K4090 ·
49. K429000
50. K579000
[ 47 others ] |
| 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 13.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 2 | ( | 5.4% | ) | | 5 | ( | 13.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 14 | ( | 37.8% | ) |
|
 |
137
(78.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_3
[factor] |
1. B1710 ·
2. B182 ·
3. E039 ·
4. E039000
5. E1169 ·
6. E119 ·
7. E119000
8. E291 ·
9. E6601 ·
10. E663 ·
11. E669 ·
12. E669000
13. E78 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E784 ·
18. E785 ·
19. E785000
20. E875 ·
21. E876 ·
22. G4730 ·
23. G4733 ·
24. G609000
25. G8929 ·
26. H409 ·
27. I10 ·
28. I100000
29. I1129 ·
30. I129 ·
31. I2510 ·
32. I495 ·
33. I50 ·
34. I509 ·
35. I700000
36. I709000
37. I739 ·
38. I7789 ·
39. I82401 ·
40. I898 ·
41. I959 ·
42. J302 ·
43. J449 ·
44. J45 ·
45. J984 ·
46. K219 ·
47. K4020 ·
48. K4090 ·
49. K429000
50. K579000
[ 47 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 1 | ( | 1.9% | ) | | 5 | ( | 9.4% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 1 | ( | 1.9% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 11.3% | ) | | 2 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 3.8% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 7.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.9% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 19 | ( | 35.8% | ) |
|
 |
237
(81.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_3
[factor] |
1. B1710 ·
2. B182 ·
3. E039 ·
4. E039000
5. E1169 ·
6. E119 ·
7. E119000
8. E291 ·
9. E6601 ·
10. E663 ·
11. E669 ·
12. E669000
13. E78 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E784 ·
18. E785 ·
19. E785000
20. E875 ·
21. E876 ·
22. G4730 ·
23. G4733 ·
24. G609000
25. G8929 ·
26. H409 ·
27. I10 ·
28. I100000
29. I1129 ·
30. I129 ·
31. I2510 ·
32. I495 ·
33. I50 ·
34. I509 ·
35. I700000
36. I709000
37. I739 ·
38. I7789 ·
39. I82401 ·
40. I898 ·
41. I959 ·
42. J302 ·
43. J449 ·
44. J45 ·
45. J984 ·
46. K219 ·
47. K4020 ·
48. K4090 ·
49. K429000
50. K579000
[ 47 others ] |
| 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 6.2% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 6 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 2.1% | ) | | 12 | ( | 12.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 5 | ( | 5.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.0% | ) | | 1 | ( | 1.0% | ) | | 40 | ( | 41.2% | ) |
|
 |
990
(91.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_3
[factor] |
1. B1710 ·
2. B182 ·
3. E039 ·
4. E039000
5. E1169 ·
6. E119 ·
7. E119000
8. E291 ·
9. E6601 ·
10. E663 ·
11. E669 ·
12. E669000
13. E78 ·
14. E780 ·
15. E7800 ·
16. E782 ·
17. E784 ·
18. E785 ·
19. E785000
20. E875 ·
21. E876 ·
22. G4730 ·
23. G4733 ·
24. G609000
25. G8929 ·
26. H409 ·
27. I10 ·
28. I100000
29. I1129 ·
30. I129 ·
31. I2510 ·
32. I495 ·
33. I50 ·
34. I509 ·
35. I700000
36. I709000
37. I739 ·
38. I7789 ·
39. I82401 ·
40. I898 ·
41. I959 ·
42. J302 ·
43. J449 ·
44. J45 ·
45. J984 ·
46. K219 ·
47. K4020 ·
48. K4090 ·
49. K429000
50. K579000
51. K5900 ·
52. K603 ·
53. L409 ·
54. M10 ·
55. M109 ·
56. M1990 ·
57. M199000
58. M47815 ·
59. M478160
60. M549 ·
61. N179 ·
62. N183 ·
63. N189 ·
64. N281 ·
65. N318 ·
66. N3941 ·
67. N40 ·
68. N400 ·
69. N401 ·
70. N4289 ·
71. N432 ·
72. N5201 ·
73. N529 ·
74. N9971 ·
75. N99842 ·
76. R05 ·
77. R200 ·
78. R3100 ·
79. R338 ·
80. R339 ·
81. R351 ·
82. R3915 ·
83. R5383 ·
84. R590 ·
85. R631 ·
86. R9431 ·
87. R972 ·
88. R9720 ·
89. Z6831 ·
90. Z801 ·
91. Z8546 ·
92. Z8673 ·
93. Z87891 ·
94. Z880 ·
95. Z955000
96. Z98890 ·
97. Z992 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 4
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3786
All data
st_css() #IMPORTANT!
secondarydiagnosis4 <- as.factor(d[,"secondarydiagnosis4"])
new.d <- data.frame(new.d, secondarydiagnosis4)
new.d <- apply_labels(new.d, secondarydiagnosis4 = "secondary_diagnosis_4")
#summary(new.d$secondarydiagnosis4)
temp.d <- data.frame (new.d.1, secondarydiagnosis4)
summarytools::view(dfSummary(new.d$secondarydiagnosis4, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis4
[labelled, factor] |
secondary_diagnosis_4 |
1. B181 ·
2. E039 ·
3. E11321 ·
4. E1139 ·
5. E1169 ·
6. E119 ·
7. E119000
8. E559 ·
9. E669 ·
10. E780 ·
11. E782 ·
12. E785 ·
13. E7850 ·
14. E871 ·
15. G4700 ·
16. G4730 ·
17. G4733 ·
18. G8929 ·
19. I10 ·
20. I100000
21. I129 ·
22. I251 ·
23. I2510 ·
24. I639000
25. I723000
26. J3089 ·
27. J3489 ·
28. J45909 ·
29. J984 ·
30. K219 ·
31. K219000
32. K44900 ·
33. K660 ·
34. L2084 ·
35. M109 ·
36. M129 ·
37. M170 ·
38. M1712 ·
39. M1990 ·
40. M1A ·
41. M25512 ·
42. M25669 ·
43. M478180
44. M5410 ·
45. M545000
46. N183 ·
47. N289 ·
48. N3289 ·
49. N40 ·
50. N401 ·
[ 22 others ] |
| 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 6 | ( | 5.2% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 2 | ( | 1.7% | ) | | 1 | ( | 0.9% | ) | | 3 | ( | 2.6% | ) | | 9 | ( | 7.8% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 2 | ( | 1.7% | ) | | 2 | ( | 1.7% | ) | | 1 | ( | 0.9% | ) | | 10 | ( | 8.7% | ) | | 1 | ( | 0.9% | ) | | 2 | ( | 1.7% | ) | | 1 | ( | 0.9% | ) | | 2 | ( | 1.7% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 2 | ( | 1.7% | ) | | 5 | ( | 4.3% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 2 | ( | 1.7% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 3 | ( | 2.6% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 1 | ( | 0.9% | ) | | 2 | ( | 1.7% | ) | | 27 | ( | 23.5% | ) |
|
 |
2052
(94.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_4
[factor] |
1. B181 ·
2. E039 ·
3. E11321 ·
4. E1139 ·
5. E1169 ·
6. E119 ·
7. E119000
8. E559 ·
9. E669 ·
10. E780 ·
11. E782 ·
12. E785 ·
13. E7850 ·
14. E871 ·
15. G4700 ·
16. G4730 ·
17. G4733 ·
18. G8929 ·
19. I10 ·
20. I100000
21. I129 ·
22. I251 ·
23. I2510 ·
24. I639000
25. I723000
26. J3089 ·
27. J3489 ·
28. J45909 ·
29. J984 ·
30. K219 ·
31. K219000
32. K44900 ·
33. K660 ·
34. L2084 ·
35. M109 ·
36. M129 ·
37. M170 ·
38. M1712 ·
39. M1990 ·
40. M1A ·
41. M25512 ·
42. M25669 ·
43. M478180
44. M5410 ·
45. M545000
46. N183 ·
47. N289 ·
48. N3289 ·
49. N40 ·
50. N401 ·
51. N521 ·
52. N528 ·
53. R0781 ·
54. R140 ·
55. R312 ·
56. R350 ·
57. R351 ·
58. R52 ·
59. R634 ·
60. R6882 ·
61. R911 ·
62. R972 ·
63. R9720 ·
64. Z6822 ·
65. Z803 ·
66. Z853 ·
67. Z86010 ·
68. Z87891 ·
69. Z878910
70. Z91013 ·
71. Z95810 ·
72. Z992 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_4
[factor] |
1. B181 ·
2. E039 ·
3. E11321 ·
4. E1139 ·
5. E1169 ·
6. E119 ·
7. E119000
8. E559 ·
9. E669 ·
10. E780 ·
11. E782 ·
12. E785 ·
13. E7850 ·
14. E871 ·
15. G4700 ·
16. G4730 ·
17. G4733 ·
18. G8929 ·
19. I10 ·
20. I100000
21. I129 ·
22. I251 ·
23. I2510 ·
24. I639000
25. I723000
26. J3089 ·
27. J3489 ·
28. J45909 ·
29. J984 ·
30. K219 ·
31. K219000
32. K44900 ·
33. K660 ·
34. L2084 ·
35. M109 ·
36. M129 ·
37. M170 ·
38. M1712 ·
39. M1990 ·
40. M1A ·
41. M25512 ·
42. M25669 ·
43. M478180
44. M5410 ·
45. M545000
46. N183 ·
47. N289 ·
48. N3289 ·
49. N40 ·
50. N401 ·
51. N521 ·
52. N528 ·
53. R0781 ·
54. R140 ·
55. R312 ·
56. R350 ·
57. R351 ·
58. R52 ·
59. R634 ·
60. R6882 ·
61. R911 ·
62. R972 ·
63. R9720 ·
64. Z6822 ·
65. Z803 ·
66. Z853 ·
67. Z86010 ·
68. Z87891 ·
69. Z878910
70. Z91013 ·
71. Z95810 ·
72. Z992 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_4
[factor] |
1. B181 ·
2. E039 ·
3. E11321 ·
4. E1139 ·
5. E1169 ·
6. E119 ·
7. E119000
8. E559 ·
9. E669 ·
10. E780 ·
11. E782 ·
12. E785 ·
13. E7850 ·
14. E871 ·
15. G4700 ·
16. G4730 ·
17. G4733 ·
18. G8929 ·
19. I10 ·
20. I100000
21. I129 ·
22. I251 ·
23. I2510 ·
24. I639000
25. I723000
26. J3089 ·
27. J3489 ·
28. J45909 ·
29. J984 ·
30. K219 ·
31. K219000
32. K44900 ·
33. K660 ·
34. L2084 ·
35. M109 ·
36. M129 ·
37. M170 ·
38. M1712 ·
39. M1990 ·
40. M1A ·
41. M25512 ·
42. M25669 ·
43. M478180
44. M5410 ·
45. M545000
46. N183 ·
47. N289 ·
48. N3289 ·
49. N40 ·
50. N401 ·
51. N521 ·
52. N528 ·
53. R0781 ·
54. R140 ·
55. R312 ·
56. R350 ·
57. R351 ·
58. R52 ·
59. R634 ·
60. R6882 ·
61. R911 ·
62. R972 ·
63. R9720 ·
64. Z6822 ·
65. Z803 ·
66. Z853 ·
67. Z86010 ·
68. Z87891 ·
69. Z878910
70. Z91013 ·
71. Z95810 ·
72. Z992 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_4
[factor] |
1. B181 ·
2. E039 ·
3. E11321 ·
4. E1139 ·
5. E1169 ·
6. E119 ·
7. E119000
8. E559 ·
9. E669 ·
10. E780 ·
11. E782 ·
12. E785 ·
13. E7850 ·
14. E871 ·
15. G4700 ·
16. G4730 ·
17. G4733 ·
18. G8929 ·
19. I10 ·
20. I100000
21. I129 ·
22. I251 ·
23. I2510 ·
24. I639000
25. I723000
26. J3089 ·
27. J3489 ·
28. J45909 ·
29. J984 ·
30. K219 ·
31. K219000
32. K44900 ·
33. K660 ·
34. L2084 ·
35. M109 ·
36. M129 ·
37. M170 ·
38. M1712 ·
39. M1990 ·
40. M1A ·
41. M25512 ·
42. M25669 ·
43. M478180
44. M5410 ·
45. M545000
46. N183 ·
47. N289 ·
48. N3289 ·
49. N40 ·
50. N401 ·
[ 22 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 5 | ( | 17.9% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.6% | ) | | 0 | ( | 0.0% | ) | | 8 | ( | 28.6% | ) |
|
 |
146
(83.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_4
[factor] |
1. B181 ·
2. E039 ·
3. E11321 ·
4. E1139 ·
5. E1169 ·
6. E119 ·
7. E119000
8. E559 ·
9. E669 ·
10. E780 ·
11. E782 ·
12. E785 ·
13. E7850 ·
14. E871 ·
15. G4700 ·
16. G4730 ·
17. G4733 ·
18. G8929 ·
19. I10 ·
20. I100000
21. I129 ·
22. I251 ·
23. I2510 ·
24. I639000
25. I723000
26. J3089 ·
27. J3489 ·
28. J45909 ·
29. J984 ·
30. K219 ·
31. K219000
32. K44900 ·
33. K660 ·
34. L2084 ·
35. M109 ·
36. M129 ·
37. M170 ·
38. M1712 ·
39. M1990 ·
40. M1A ·
41. M25512 ·
42. M25669 ·
43. M478180
44. M5410 ·
45. M545000
46. N183 ·
47. N289 ·
48. N3289 ·
49. N40 ·
50. N401 ·
[ 22 others ] |
| 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 1 | ( | 3.8% | ) | | 1 | ( | 3.8% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 1 | ( | 3.8% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 7.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.8% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 7.7% | ) | | 5 | ( | 19.2% | ) |
|
 |
264
(91.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_4
[factor] |
1. B181 ·
2. E039 ·
3. E11321 ·
4. E1139 ·
5. E1169 ·
6. E119 ·
7. E119000
8. E559 ·
9. E669 ·
10. E780 ·
11. E782 ·
12. E785 ·
13. E7850 ·
14. E871 ·
15. G4700 ·
16. G4730 ·
17. G4733 ·
18. G8929 ·
19. I10 ·
20. I100000
21. I129 ·
22. I251 ·
23. I2510 ·
24. I639000
25. I723000
26. J3089 ·
27. J3489 ·
28. J45909 ·
29. J984 ·
30. K219 ·
31. K219000
32. K44900 ·
33. K660 ·
34. L2084 ·
35. M109 ·
36. M129 ·
37. M170 ·
38. M1712 ·
39. M1990 ·
40. M1A ·
41. M25512 ·
42. M25669 ·
43. M478180
44. M5410 ·
45. M545000
46. N183 ·
47. N289 ·
48. N3289 ·
49. N40 ·
50. N401 ·
[ 22 others ] |
| 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 5 | ( | 8.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 2 | ( | 3.3% | ) | | 6 | ( | 9.8% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 6.6% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 6.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 1.6% | ) | | 1 | ( | 1.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 14 | ( | 23.0% | ) |
|
 |
1026
(94.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_4
[factor] |
1. B181 ·
2. E039 ·
3. E11321 ·
4. E1139 ·
5. E1169 ·
6. E119 ·
7. E119000
8. E559 ·
9. E669 ·
10. E780 ·
11. E782 ·
12. E785 ·
13. E7850 ·
14. E871 ·
15. G4700 ·
16. G4730 ·
17. G4733 ·
18. G8929 ·
19. I10 ·
20. I100000
21. I129 ·
22. I251 ·
23. I2510 ·
24. I639000
25. I723000
26. J3089 ·
27. J3489 ·
28. J45909 ·
29. J984 ·
30. K219 ·
31. K219000
32. K44900 ·
33. K660 ·
34. L2084 ·
35. M109 ·
36. M129 ·
37. M170 ·
38. M1712 ·
39. M1990 ·
40. M1A ·
41. M25512 ·
42. M25669 ·
43. M478180
44. M5410 ·
45. M545000
46. N183 ·
47. N289 ·
48. N3289 ·
49. N40 ·
50. N401 ·
51. N521 ·
52. N528 ·
53. R0781 ·
54. R140 ·
55. R312 ·
56. R350 ·
57. R351 ·
58. R52 ·
59. R634 ·
60. R6882 ·
61. R911 ·
62. R972 ·
63. R9720 ·
64. Z6822 ·
65. Z803 ·
66. Z853 ·
67. Z86010 ·
68. Z87891 ·
69. Z878910
70. Z91013 ·
71. Z95810 ·
72. Z992 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 5
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3788
All data
st_css() #IMPORTANT!
secondarydiagnosis5 <- as.factor(d[,"secondarydiagnosis5"])
new.d <- data.frame(new.d, secondarydiagnosis5)
new.d <- apply_labels(new.d, secondarydiagnosis5 = "secondary_diagnosis_5")
#summary(new.d$secondarydiagnosis5)
temp.d <- data.frame (new.d.1, secondarydiagnosis5)
summarytools::view(dfSummary(new.d$secondarydiagnosis5, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis5
[labelled, factor] |
secondary_diagnosis_5 |
1. B009 ·
2. E039 ·
3. E042 ·
4. E119 ·
5. E669 ·
6. E785 ·
7. G4730 ·
8. G629 ·
9. H3552 ·
10. I071 ·
11. I10 ·
12. I2510 ·
13. I252 ·
14. I420 ·
15. I739 ·
16. J309 ·
17. J449 ·
18. K219 ·
19. K219000
20. M109 ·
21. M19011 ·
22. M1990 ·
23. M25122 ·
24. M25561 ·
25. M478150
26. M480200
27. M542 ·
28. M549 ·
29. M7581 ·
30. N138 ·
31. N182 ·
32. N183 ·
33. N189 ·
34. N40 ·
35. N400 ·
36. N401 ·
37. N411 ·
38. R0602 ·
39. R300 ·
40. R350 ·
41. R351 ·
42. R739 ·
43. R740 ·
44. R809 ·
45. R972 ·
46. Z8042 ·
47. Z87891 ·
48. Z9079 ·
49. Z9221 ·
50. Z940 ·
[ 2 others ] |
| 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 2 | ( | 2.9% | ) | | 2 | ( | 2.9% | ) | | 3 | ( | 4.3% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 3 | ( | 4.3% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 3 | ( | 4.3% | ) | | 4 | ( | 5.7% | ) | | 1 | ( | 1.4% | ) | | 3 | ( | 4.3% | ) | | 1 | ( | 1.4% | ) | | 2 | ( | 2.9% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 2 | ( | 2.9% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 3 | ( | 4.3% | ) | | 2 | ( | 2.9% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 1 | ( | 1.4% | ) | | 2 | ( | 2.9% | ) |
|
 |
2097
(96.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_5
[factor] |
1. B009 ·
2. E039 ·
3. E042 ·
4. E119 ·
5. E669 ·
6. E785 ·
7. G4730 ·
8. G629 ·
9. H3552 ·
10. I071 ·
11. I10 ·
12. I2510 ·
13. I252 ·
14. I420 ·
15. I739 ·
16. J309 ·
17. J449 ·
18. K219 ·
19. K219000
20. M109 ·
21. M19011 ·
22. M1990 ·
23. M25122 ·
24. M25561 ·
25. M478150
26. M480200
27. M542 ·
28. M549 ·
29. M7581 ·
30. N138 ·
31. N182 ·
32. N183 ·
33. N189 ·
34. N40 ·
35. N400 ·
36. N401 ·
37. N411 ·
38. R0602 ·
39. R300 ·
40. R350 ·
41. R351 ·
42. R739 ·
43. R740 ·
44. R809 ·
45. R972 ·
46. Z8042 ·
47. Z87891 ·
48. Z9079 ·
49. Z9221 ·
50. Z940 ·
51. Z951 ·
52. Z96643 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_5
[factor] |
1. B009 ·
2. E039 ·
3. E042 ·
4. E119 ·
5. E669 ·
6. E785 ·
7. G4730 ·
8. G629 ·
9. H3552 ·
10. I071 ·
11. I10 ·
12. I2510 ·
13. I252 ·
14. I420 ·
15. I739 ·
16. J309 ·
17. J449 ·
18. K219 ·
19. K219000
20. M109 ·
21. M19011 ·
22. M1990 ·
23. M25122 ·
24. M25561 ·
25. M478150
26. M480200
27. M542 ·
28. M549 ·
29. M7581 ·
30. N138 ·
31. N182 ·
32. N183 ·
33. N189 ·
34. N40 ·
35. N400 ·
36. N401 ·
37. N411 ·
38. R0602 ·
39. R300 ·
40. R350 ·
41. R351 ·
42. R739 ·
43. R740 ·
44. R809 ·
45. R972 ·
46. Z8042 ·
47. Z87891 ·
48. Z9079 ·
49. Z9221 ·
50. Z940 ·
51. Z951 ·
52. Z96643 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_5
[factor] |
1. B009 ·
2. E039 ·
3. E042 ·
4. E119 ·
5. E669 ·
6. E785 ·
7. G4730 ·
8. G629 ·
9. H3552 ·
10. I071 ·
11. I10 ·
12. I2510 ·
13. I252 ·
14. I420 ·
15. I739 ·
16. J309 ·
17. J449 ·
18. K219 ·
19. K219000
20. M109 ·
21. M19011 ·
22. M1990 ·
23. M25122 ·
24. M25561 ·
25. M478150
26. M480200
27. M542 ·
28. M549 ·
29. M7581 ·
30. N138 ·
31. N182 ·
32. N183 ·
33. N189 ·
34. N40 ·
35. N400 ·
36. N401 ·
37. N411 ·
38. R0602 ·
39. R300 ·
40. R350 ·
41. R351 ·
42. R739 ·
43. R740 ·
44. R809 ·
45. R972 ·
46. Z8042 ·
47. Z87891 ·
48. Z9079 ·
49. Z9221 ·
50. Z940 ·
51. Z951 ·
52. Z96643 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_5
[factor] |
1. B009 ·
2. E039 ·
3. E042 ·
4. E119 ·
5. E669 ·
6. E785 ·
7. G4730 ·
8. G629 ·
9. H3552 ·
10. I071 ·
11. I10 ·
12. I2510 ·
13. I252 ·
14. I420 ·
15. I739 ·
16. J309 ·
17. J449 ·
18. K219 ·
19. K219000
20. M109 ·
21. M19011 ·
22. M1990 ·
23. M25122 ·
24. M25561 ·
25. M478150
26. M480200
27. M542 ·
28. M549 ·
29. M7581 ·
30. N138 ·
31. N182 ·
32. N183 ·
33. N189 ·
34. N40 ·
35. N400 ·
36. N401 ·
37. N411 ·
38. R0602 ·
39. R300 ·
40. R350 ·
41. R351 ·
42. R739 ·
43. R740 ·
44. R809 ·
45. R972 ·
46. Z8042 ·
47. Z87891 ·
48. Z9079 ·
49. Z9221 ·
50. Z940 ·
[ 2 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 1 | ( | 5.3% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 10.5% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 5.3% | ) | | 1 | ( | 5.3% | ) | | 1 | ( | 5.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
155
(89.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_5
[factor] |
1. B009 ·
2. E039 ·
3. E042 ·
4. E119 ·
5. E669 ·
6. E785 ·
7. G4730 ·
8. G629 ·
9. H3552 ·
10. I071 ·
11. I10 ·
12. I2510 ·
13. I252 ·
14. I420 ·
15. I739 ·
16. J309 ·
17. J449 ·
18. K219 ·
19. K219000
20. M109 ·
21. M19011 ·
22. M1990 ·
23. M25122 ·
24. M25561 ·
25. M478150
26. M480200
27. M542 ·
28. M549 ·
29. M7581 ·
30. N138 ·
31. N182 ·
32. N183 ·
33. N189 ·
34. N40 ·
35. N400 ·
36. N401 ·
37. N411 ·
38. R0602 ·
39. R300 ·
40. R350 ·
41. R351 ·
42. R739 ·
43. R740 ·
44. R809 ·
45. R972 ·
46. Z8042 ·
47. Z87891 ·
48. Z9079 ·
49. Z9221 ·
50. Z940 ·
[ 2 others ] |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 1 | ( | 7.1% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 7.1% | ) | | 1 | ( | 7.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
276
(95.2%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_5
[factor] |
1. B009 ·
2. E039 ·
3. E042 ·
4. E119 ·
5. E669 ·
6. E785 ·
7. G4730 ·
8. G629 ·
9. H3552 ·
10. I071 ·
11. I10 ·
12. I2510 ·
13. I252 ·
14. I420 ·
15. I739 ·
16. J309 ·
17. J449 ·
18. K219 ·
19. K219000
20. M109 ·
21. M19011 ·
22. M1990 ·
23. M25122 ·
24. M25561 ·
25. M478150
26. M480200
27. M542 ·
28. M549 ·
29. M7581 ·
30. N138 ·
31. N182 ·
32. N183 ·
33. N189 ·
34. N40 ·
35. N400 ·
36. N401 ·
37. N411 ·
38. R0602 ·
39. R300 ·
40. R350 ·
41. R351 ·
42. R739 ·
43. R740 ·
44. R809 ·
45. R972 ·
46. Z8042 ·
47. Z87891 ·
48. Z9079 ·
49. Z9221 ·
50. Z940 ·
[ 2 others ] |
| 1 | ( | 2.7% | ) | | 1 | ( | 2.7% | ) | | 1 | ( | 2.7% | ) | | 1 | ( | 2.7% | ) | | 1 | ( | 2.7% | ) | | 3 | ( | 8.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 3 | ( | 8.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 2 | ( | 5.4% | ) | | 4 | ( | 10.8% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 1 | ( | 2.7% | ) | | 1 | ( | 2.7% | ) | | 1 | ( | 2.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 2.7% | ) | | 2 | ( | 5.4% | ) |
|
 |
1050
(96.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_5
[factor] |
1. B009 ·
2. E039 ·
3. E042 ·
4. E119 ·
5. E669 ·
6. E785 ·
7. G4730 ·
8. G629 ·
9. H3552 ·
10. I071 ·
11. I10 ·
12. I2510 ·
13. I252 ·
14. I420 ·
15. I739 ·
16. J309 ·
17. J449 ·
18. K219 ·
19. K219000
20. M109 ·
21. M19011 ·
22. M1990 ·
23. M25122 ·
24. M25561 ·
25. M478150
26. M480200
27. M542 ·
28. M549 ·
29. M7581 ·
30. N138 ·
31. N182 ·
32. N183 ·
33. N189 ·
34. N40 ·
35. N400 ·
36. N401 ·
37. N411 ·
38. R0602 ·
39. R300 ·
40. R350 ·
41. R351 ·
42. R739 ·
43. R740 ·
44. R809 ·
45. R972 ·
46. Z8042 ·
47. Z87891 ·
48. Z9079 ·
49. Z9221 ·
50. Z940 ·
51. Z951 ·
52. Z96643 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 6
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3790
All data
st_css() #IMPORTANT!
secondarydiagnosis6 <- as.factor(d[,"secondarydiagnosis6"])
new.d <- data.frame(new.d, secondarydiagnosis6)
new.d <- apply_labels(new.d, secondarydiagnosis6 = "secondary_diagnosis_6")
#summary(new.d$secondarydiagnosis6)
temp.d <- data.frame (new.d.1, secondarydiagnosis6)
summarytools::view(dfSummary(new.d$secondarydiagnosis6, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis6
[labelled, factor] |
secondary_diagnosis_6 |
1. E041 ·
2. E1122 ·
3. E119 ·
4. E119000
5. E669 ·
6. E780 ·
7. E7800 ·
8. E785 ·
9. E875 ·
10. F328 ·
11. G4730 ·
12. I10 ·
13. I251 ·
14. I429 ·
15. I7090 ·
16. J45 ·
17. K219 ·
18. K3580 ·
19. M109 ·
20. M199 ·
21. M1990 ·
22. M545 ·
23. N136 ·
24. N189 ·
25. N40 ·
26. N419 ·
27. R339 ·
28. R351 ·
29. R599 ·
30. R9431 ·
31. R972 ·
32. Y658 ·
33. Z6837 ·
34. Z86718 · |
| 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 2 | ( | 4.2% | ) | | 1 | ( | 2.1% | ) | | 2 | ( | 4.2% | ) | | 3 | ( | 6.2% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 2 | ( | 4.2% | ) | | 5 | ( | 10.4% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 2 | ( | 4.2% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 3 | ( | 6.2% | ) | | 1 | ( | 2.1% | ) | | 1 | ( | 2.1% | ) | | 2 | ( | 4.2% | ) | | 1 | ( | 2.1% | ) | | 2 | ( | 4.2% | ) | | 1 | ( | 2.1% | ) |
|
 |
2119
(97.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_6
[factor] |
1. E041 ·
2. E1122 ·
3. E119 ·
4. E119000
5. E669 ·
6. E780 ·
7. E7800 ·
8. E785 ·
9. E875 ·
10. F328 ·
11. G4730 ·
12. I10 ·
13. I251 ·
14. I429 ·
15. I7090 ·
16. J45 ·
17. K219 ·
18. K3580 ·
19. M109 ·
20. M199 ·
21. M1990 ·
22. M545 ·
23. N136 ·
24. N189 ·
25. N40 ·
26. N419 ·
27. R339 ·
28. R351 ·
29. R599 ·
30. R9431 ·
31. R972 ·
32. Y658 ·
33. Z6837 ·
34. Z86718 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_6
[factor] |
1. E041 ·
2. E1122 ·
3. E119 ·
4. E119000
5. E669 ·
6. E780 ·
7. E7800 ·
8. E785 ·
9. E875 ·
10. F328 ·
11. G4730 ·
12. I10 ·
13. I251 ·
14. I429 ·
15. I7090 ·
16. J45 ·
17. K219 ·
18. K3580 ·
19. M109 ·
20. M199 ·
21. M1990 ·
22. M545 ·
23. N136 ·
24. N189 ·
25. N40 ·
26. N419 ·
27. R339 ·
28. R351 ·
29. R599 ·
30. R9431 ·
31. R972 ·
32. Y658 ·
33. Z6837 ·
34. Z86718 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_6
[factor] |
1. E041 ·
2. E1122 ·
3. E119 ·
4. E119000
5. E669 ·
6. E780 ·
7. E7800 ·
8. E785 ·
9. E875 ·
10. F328 ·
11. G4730 ·
12. I10 ·
13. I251 ·
14. I429 ·
15. I7090 ·
16. J45 ·
17. K219 ·
18. K3580 ·
19. M109 ·
20. M199 ·
21. M1990 ·
22. M545 ·
23. N136 ·
24. N189 ·
25. N40 ·
26. N419 ·
27. R339 ·
28. R351 ·
29. R599 ·
30. R9431 ·
31. R972 ·
32. Y658 ·
33. Z6837 ·
34. Z86718 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_6
[factor] |
1. E041 ·
2. E1122 ·
3. E119 ·
4. E119000
5. E669 ·
6. E780 ·
7. E7800 ·
8. E785 ·
9. E875 ·
10. F328 ·
11. G4730 ·
12. I10 ·
13. I251 ·
14. I429 ·
15. I7090 ·
16. J45 ·
17. K219 ·
18. K3580 ·
19. M109 ·
20. M199 ·
21. M1990 ·
22. M545 ·
23. N136 ·
24. N189 ·
25. N40 ·
26. N419 ·
27. R339 ·
28. R351 ·
29. R599 ·
30. R9431 ·
31. R972 ·
32. Y658 ·
33. Z6837 ·
34. Z86718 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) | | 2 | ( | 18.2% | ) | | 1 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 9.1% | ) | | 1 | ( | 9.1% | ) |
|
 |
163
(93.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_6
[factor] |
1. E041 ·
2. E1122 ·
3. E119 ·
4. E119000
5. E669 ·
6. E780 ·
7. E7800 ·
8. E785 ·
9. E875 ·
10. F328 ·
11. G4730 ·
12. I10 ·
13. I251 ·
14. I429 ·
15. I7090 ·
16. J45 ·
17. K219 ·
18. K3580 ·
19. M109 ·
20. M199 ·
21. M1990 ·
22. M545 ·
23. N136 ·
24. N189 ·
25. N40 ·
26. N419 ·
27. R339 ·
28. R351 ·
29. R599 ·
30. R9431 ·
31. R972 ·
32. Y658 ·
33. Z6837 ·
34. Z86718 · |
| 1 | ( | 10.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 10.0% | ) | | 1 | ( | 10.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 10.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 10.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 10.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 10.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 10.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
280
(96.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_6
[factor] |
1. E041 ·
2. E1122 ·
3. E119 ·
4. E119000
5. E669 ·
6. E780 ·
7. E7800 ·
8. E785 ·
9. E875 ·
10. F328 ·
11. G4730 ·
12. I10 ·
13. I251 ·
14. I429 ·
15. I7090 ·
16. J45 ·
17. K219 ·
18. K3580 ·
19. M109 ·
20. M199 ·
21. M1990 ·
22. M545 ·
23. N136 ·
24. N189 ·
25. N40 ·
26. N419 ·
27. R339 ·
28. R351 ·
29. R599 ·
30. R9431 ·
31. R972 ·
32. Y658 ·
33. Z6837 ·
34. Z86718 · |
| 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 1 | ( | 3.7% | ) | | 2 | ( | 7.4% | ) | | 3 | ( | 11.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 2 | ( | 7.4% | ) | | 3 | ( | 11.1% | ) | | 1 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 1 | ( | 3.7% | ) | | 1 | ( | 3.7% | ) | | 1 | ( | 3.7% | ) | | 1 | ( | 3.7% | ) | | 1 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.7% | ) | | 1 | ( | 3.7% | ) | | 0 | ( | 0.0% | ) |
|
 |
1060
(97.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_6
[factor] |
1. E041 ·
2. E1122 ·
3. E119 ·
4. E119000
5. E669 ·
6. E780 ·
7. E7800 ·
8. E785 ·
9. E875 ·
10. F328 ·
11. G4730 ·
12. I10 ·
13. I251 ·
14. I429 ·
15. I7090 ·
16. J45 ·
17. K219 ·
18. K3580 ·
19. M109 ·
20. M199 ·
21. M1990 ·
22. M545 ·
23. N136 ·
24. N189 ·
25. N40 ·
26. N419 ·
27. R339 ·
28. R351 ·
29. R599 ·
30. R9431 ·
31. R972 ·
32. Y658 ·
33. Z6837 ·
34. Z86718 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 7
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3792
All data
st_css() #IMPORTANT!
secondarydiagnosis7 <- as.factor(d[,"secondarydiagnosis7"])
new.d <- data.frame(new.d, secondarydiagnosis7)
new.d <- apply_labels(new.d, secondarydiagnosis7 = "secondary_diagnosis_7")
#summary(new.d$secondarydiagnosis7)
temp.d <- data.frame (new.d.1, secondarydiagnosis7)
summarytools::view(dfSummary(new.d$secondarydiagnosis7, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis7
[labelled, factor] |
secondary_diagnosis_7 |
1. E119 ·
2. E669 ·
3. E780 ·
4. E781 ·
5. E785 ·
6. E8352 ·
7. G473 ·
8. H409 ·
9. I2510 ·
10. I252000
11. I4891 ·
12. J189 ·
13. M069 ·
14. M791 ·
15. N400 ·
16. N401 ·
17. N5201 ·
18. N529 ·
19. R310 ·
20. R7303 ·
21. R740 ·
22. Y838 ·
23. Z6831 ·
24. Z8042 ·
25. Z86711 ·
26. Z885 ·
27. Z9049 · |
| 1 | ( | 3.3% | ) | | 2 | ( | 6.7% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 2 | ( | 6.7% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 2 | ( | 6.7% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) | | 1 | ( | 3.3% | ) |
|
 |
2137
(98.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_7
[factor] |
1. E119 ·
2. E669 ·
3. E780 ·
4. E781 ·
5. E785 ·
6. E8352 ·
7. G473 ·
8. H409 ·
9. I2510 ·
10. I252000
11. I4891 ·
12. J189 ·
13. M069 ·
14. M791 ·
15. N400 ·
16. N401 ·
17. N5201 ·
18. N529 ·
19. R310 ·
20. R7303 ·
21. R740 ·
22. Y838 ·
23. Z6831 ·
24. Z8042 ·
25. Z86711 ·
26. Z885 ·
27. Z9049 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_7
[factor] |
1. E119 ·
2. E669 ·
3. E780 ·
4. E781 ·
5. E785 ·
6. E8352 ·
7. G473 ·
8. H409 ·
9. I2510 ·
10. I252000
11. I4891 ·
12. J189 ·
13. M069 ·
14. M791 ·
15. N400 ·
16. N401 ·
17. N5201 ·
18. N529 ·
19. R310 ·
20. R7303 ·
21. R740 ·
22. Y838 ·
23. Z6831 ·
24. Z8042 ·
25. Z86711 ·
26. Z885 ·
27. Z9049 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_7
[factor] |
1. E119 ·
2. E669 ·
3. E780 ·
4. E781 ·
5. E785 ·
6. E8352 ·
7. G473 ·
8. H409 ·
9. I2510 ·
10. I252000
11. I4891 ·
12. J189 ·
13. M069 ·
14. M791 ·
15. N400 ·
16. N401 ·
17. N5201 ·
18. N529 ·
19. R310 ·
20. R7303 ·
21. R740 ·
22. Y838 ·
23. Z6831 ·
24. Z8042 ·
25. Z86711 ·
26. Z885 ·
27. Z9049 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_7
[factor] |
1. E119 ·
2. E669 ·
3. E780 ·
4. E781 ·
5. E785 ·
6. E8352 ·
7. G473 ·
8. H409 ·
9. I2510 ·
10. I252000
11. I4891 ·
12. J189 ·
13. M069 ·
14. M791 ·
15. N400 ·
16. N401 ·
17. N5201 ·
18. N529 ·
19. R310 ·
20. R7303 ·
21. R740 ·
22. Y838 ·
23. Z6831 ·
24. Z8042 ·
25. Z86711 ·
26. Z885 ·
27. Z9049 · |
| 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
167
(96.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_7
[factor] |
1. E119 ·
2. E669 ·
3. E780 ·
4. E781 ·
5. E785 ·
6. E8352 ·
7. G473 ·
8. H409 ·
9. I2510 ·
10. I252000
11. I4891 ·
12. J189 ·
13. M069 ·
14. M791 ·
15. N400 ·
16. N401 ·
17. N5201 ·
18. N529 ·
19. R310 ·
20. R7303 ·
21. R740 ·
22. Y838 ·
23. Z6831 ·
24. Z8042 ·
25. Z86711 ·
26. Z885 ·
27. Z9049 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) |
|
 |
283
(97.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_7
[factor] |
1. E119 ·
2. E669 ·
3. E780 ·
4. E781 ·
5. E785 ·
6. E8352 ·
7. G473 ·
8. H409 ·
9. I2510 ·
10. I252000
11. I4891 ·
12. J189 ·
13. M069 ·
14. M791 ·
15. N400 ·
16. N401 ·
17. N5201 ·
18. N529 ·
19. R310 ·
20. R7303 ·
21. R740 ·
22. Y838 ·
23. Z6831 ·
24. Z8042 ·
25. Z86711 ·
26. Z885 ·
27. Z9049 · |
| 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 2 | ( | 12.5% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 6.2% | ) |
|
 |
1071
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_7
[factor] |
1. E119 ·
2. E669 ·
3. E780 ·
4. E781 ·
5. E785 ·
6. E8352 ·
7. G473 ·
8. H409 ·
9. I2510 ·
10. I252000
11. I4891 ·
12. J189 ·
13. M069 ·
14. M791 ·
15. N400 ·
16. N401 ·
17. N5201 ·
18. N529 ·
19. R310 ·
20. R7303 ·
21. R740 ·
22. Y838 ·
23. Z6831 ·
24. Z8042 ·
25. Z86711 ·
26. Z885 ·
27. Z9049 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 8
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3794
All data
st_css() #IMPORTANT!
secondarydiagnosis8 <- as.factor(d[,"secondarydiagnosis8"])
new.d <- data.frame(new.d, secondarydiagnosis8)
new.d <- apply_labels(new.d, secondarydiagnosis8 = "secondary_diagnosis_8")
#summary(new.d$secondarydiagnosis8)
temp.d <- data.frame (new.d.1, secondarydiagnosis8)
summarytools::view(dfSummary(new.d$secondarydiagnosis8, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis8
[labelled, factor] |
secondary_diagnosis_8 |
1. E291 ·
2. E669000
3. E876 ·
4. G893 ·
5. I129 ·
6. I509 ·
7. I714 ·
8. J189 ·
9. N400 ·
10. R319 ·
11. R5383 ·
12. R791 ·
13. Z6833 ·
14. Z6836 ·
15. Z87891 ·
16. Z9079 · |
| 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) | | 1 | ( | 6.2% | ) |
|
 |
2151
(99.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_8
[factor] |
1. E291 ·
2. E669000
3. E876 ·
4. G893 ·
5. I129 ·
6. I509 ·
7. I714 ·
8. J189 ·
9. N400 ·
10. R319 ·
11. R5383 ·
12. R791 ·
13. Z6833 ·
14. Z6836 ·
15. Z87891 ·
16. Z9079 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_8
[factor] |
1. E291 ·
2. E669000
3. E876 ·
4. G893 ·
5. I129 ·
6. I509 ·
7. I714 ·
8. J189 ·
9. N400 ·
10. R319 ·
11. R5383 ·
12. R791 ·
13. Z6833 ·
14. Z6836 ·
15. Z87891 ·
16. Z9079 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_8
[factor] |
1. E291 ·
2. E669000
3. E876 ·
4. G893 ·
5. I129 ·
6. I509 ·
7. I714 ·
8. J189 ·
9. N400 ·
10. R319 ·
11. R5383 ·
12. R791 ·
13. Z6833 ·
14. Z6836 ·
15. Z87891 ·
16. Z9079 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_8
[factor] |
1. E291 ·
2. E669000
3. E876 ·
4. G893 ·
5. I129 ·
6. I509 ·
7. I714 ·
8. J189 ·
9. N400 ·
10. R319 ·
11. R5383 ·
12. R791 ·
13. Z6833 ·
14. Z6836 ·
15. Z87891 ·
16. Z9079 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 16.7% | ) | | 1 | ( | 16.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 16.7% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 16.7% | ) | | 1 | ( | 16.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 16.7% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
168
(96.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_8
[factor] |
1. E291 ·
2. E669000
3. E876 ·
4. G893 ·
5. I129 ·
6. I509 ·
7. I714 ·
8. J189 ·
9. N400 ·
10. R319 ·
11. R5383 ·
12. R791 ·
13. Z6833 ·
14. Z6836 ·
15. Z87891 ·
16. Z9079 · |
| 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) |
|
 |
285
(98.3%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_8
[factor] |
1. E291 ·
2. E669000
3. E876 ·
4. G893 ·
5. I129 ·
6. I509 ·
7. I714 ·
8. J189 ·
9. N400 ·
10. R319 ·
11. R5383 ·
12. R791 ·
13. Z6833 ·
14. Z6836 ·
15. Z87891 ·
16. Z9079 · |
| 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1082
(99.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_8
[factor] |
1. E291 ·
2. E669000
3. E876 ·
4. G893 ·
5. I129 ·
6. I509 ·
7. I714 ·
8. J189 ·
9. N400 ·
10. R319 ·
11. R5383 ·
12. R791 ·
13. Z6833 ·
14. Z6836 ·
15. Z87891 ·
16. Z9079 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 9
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3796
All data
st_css() #IMPORTANT!
secondarydiagnosis9 <- as.factor(d[,"secondarydiagnosis9"])
new.d <- data.frame(new.d, secondarydiagnosis9)
new.d <- apply_labels(new.d, secondarydiagnosis9 = "secondary_diagnosis_9")
#summary(new.d$secondarydiagnosis9)
temp.d <- data.frame (new.d.1, secondarydiagnosis9)
summarytools::view(dfSummary(new.d$secondarydiagnosis9, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis9
[labelled, factor] |
secondary_diagnosis_9 |
1. E669 ·
2. I2510 ·
3. M109 ·
4. M1990 ·
5. N179 ·
6. N400 ·
7. N529 ·
8. R61 ·
9. Z683700
10. Z882 ·
11. Z9089 · |
| 2 | ( | 16.7% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) | | 1 | ( | 8.3% | ) |
|
 |
2155
(99.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_9
[factor] |
1. E669 ·
2. I2510 ·
3. M109 ·
4. M1990 ·
5. N179 ·
6. N400 ·
7. N529 ·
8. R61 ·
9. Z683700
10. Z882 ·
11. Z9089 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_9
[factor] |
1. E669 ·
2. I2510 ·
3. M109 ·
4. M1990 ·
5. N179 ·
6. N400 ·
7. N529 ·
8. R61 ·
9. Z683700
10. Z882 ·
11. Z9089 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_9
[factor] |
1. E669 ·
2. I2510 ·
3. M109 ·
4. M1990 ·
5. N179 ·
6. N400 ·
7. N529 ·
8. R61 ·
9. Z683700
10. Z882 ·
11. Z9089 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_9
[factor] |
1. E669 ·
2. I2510 ·
3. M109 ·
4. M1990 ·
5. N179 ·
6. N400 ·
7. N529 ·
8. R61 ·
9. Z683700
10. Z882 ·
11. Z9089 · |
| 2 | ( | 40.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 20.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
169
(97.1%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_9
[factor] |
1. E669 ·
2. I2510 ·
3. M109 ·
4. M1990 ·
5. N179 ·
6. N400 ·
7. N529 ·
8. R61 ·
9. Z683700
10. Z882 ·
11. Z9089 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) | | 1 | ( | 25.0% | ) |
|
 |
286
(98.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_9
[factor] |
1. E669 ·
2. I2510 ·
3. M109 ·
4. M1990 ·
5. N179 ·
6. N400 ·
7. N529 ·
8. R61 ·
9. Z683700
10. Z882 ·
11. Z9089 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1084
(99.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_9
[factor] |
1. E669 ·
2. I2510 ·
3. M109 ·
4. M1990 ·
5. N179 ·
6. N400 ·
7. N529 ·
8. R61 ·
9. Z683700
10. Z882 ·
11. Z9089 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
SECONDARY DIAGNOSIS 10
Description: Records the patient’s preexisting medical conditions, factors influencing health status, and/or complications for the treatment of this cancer. Both are considered secondary diagnoses. Preexisting medical conditions, factors influencing health status, and/or complications may affect treatment decisions and influence patient outcomes. Information on comorbidities is used to adjust outcome statistics when evaluating patient survival and other outcomes. Complications may be related to the quality of care. ICD-10-CM codes are 7 characters long, where each character represents an aspect of the condition or procedure: the 7 characters indicate ‘section’, ‘body system’, ‘root operation’, ‘body part’, ‘approach’, ‘device’, and ‘qualifier’, respectively (see ICD-10-PCS Reference Manual for additional information).
Rationale: The current Comorbidity and complication items are based on ICD-9-CM codes and only allow 5 characters, with the introduction of ICD-10-CM in to common use the NAACCR transmission record needs to be able to carry these new codes (that are longer in length and different in structure).
Codes
- A00.0 - B99.9 infectious and parasitic diseases
- E00.0 - E89.89 endocrine and metabolic diseases
- G00.0 - P96.9 diseases of the nervous system, eye, ear, skin, circulatory, respiratory, and digestive , musculoskeletal, genitourinary systems, pregnancy, childbirth and perinatal conditions
- R00.0 - S99.929 symptoms, signs and abnormal clinical and lab findings
- T36.0 - T50.996 medical poisonings
- Y62.0 - Y84.9 medical misadventures
- Z14.0 - Z22.9 genetic susceptibility / infection disease carrier
- Z68.1 - Z68.54 BMI
- Z80.0 - Z80.9 family history of malignant neoplasms
- Z85.0 - Z86.03 personal history of malignant neoplasms
- Z86.1 - Z99.89 other personal health status
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3798
All data
st_css() #IMPORTANT!
secondarydiagnosis10 <- as.factor(d[,"secondarydiagnosis10"])
new.d <- data.frame(new.d, secondarydiagnosis10)
new.d <- apply_labels(new.d, secondarydiagnosis10 = "secondary_diagnosis_10")
#summary(new.d$secondarydiagnosis10)
temp.d <- data.frame (new.d.1, secondarydiagnosis10)
summarytools::view(dfSummary(new.d$secondarydiagnosis10, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondarydiagnosis10
[labelled, factor] |
secondary_diagnosis_10 |
1. E039000
2. E785 ·
3. J329 ·
4. M25552 ·
5. N281 ·
6. Y836 · |
| 1 | ( | 16.7% | ) | | 1 | ( | 16.7% | ) | | 1 | ( | 16.7% | ) | | 1 | ( | 16.7% | ) | | 1 | ( | 16.7% | ) | | 1 | ( | 16.7% | ) |
|
 |
2161
(99.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_10
[factor] |
1. E039000
2. E785 ·
3. J329 ·
4. M25552 ·
5. N281 ·
6. Y836 · |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_10
[factor] |
1. E039000
2. E785 ·
3. J329 ·
4. M25552 ·
5. N281 ·
6. Y836 · |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_10
[factor] |
1. E039000
2. E785 ·
3. J329 ·
4. M25552 ·
5. N281 ·
6. Y836 · |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_10
[factor] |
1. E039000
2. E785 ·
3. J329 ·
4. M25552 ·
5. N281 ·
6. Y836 · |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
173
(99.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_10
[factor] |
1. E039000
2. E785 ·
3. J329 ·
4. M25552 ·
5. N281 ·
6. Y836 · |
| 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 33.3% | ) |
|
 |
287
(99.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_10
[factor] |
1. E039000
2. E785 ·
3. J329 ·
4. M25552 ·
5. N281 ·
6. Y836 · |
| 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
secondary_diagnosis_10
[factor] |
1. E039000
2. E785 ·
3. J329 ·
4. M25552 ·
5. N281 ·
6. Y836 · |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
GLEASON PATTERNS CLINICAL
Description: Prostate cancers are graded using Gleason score or pattern. This data item represents the Gleason primary and secondary patterns from needle core biopsy or TURP.
Rationale: Gleason Patterns Clinical is a Registry Data Collection Variable for Clinical Stage for AJCC. This data item was previously collected as Prostate, CS SSF# 7
Codes
- 11 Primary pattern 1, secondary pattern 1
- 13 Primary pattern 1, secondary pattern 3
- 23 Primary pattern 2, secondary pattern 3
- 25 Primary pattern 2, secondary pattern 5
- 33 Primary pattern 3, secondary pattern 3
- 34 Primary pattern 3, secondary pattern 4
- 35 Primary pattern 3, secondary pattern 5
- 39 Primary pattern 3, secondary pattern unknown
- 43 Primary pattern 4, secondary pattern 3
- 44 Primary pattern 4, secondary pattern 4
- 45 Primary pattern 4, secondary pattern 5
- 53 Primary pattern 5, secondary pattern 3
- 54 Primary pattern 5, secondary pattern 4
- 55 Primary pattern 5, secondary pattern 5
- X7 No needle core biopsy/TURP performed
- X9 Not documented in medical record/Gleason Patterns Clinical not assessed or unknown if assessed Unknown whether TURP and/or Biopsy done
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3838
All data
st_css() #IMPORTANT!
gleasonpatternsclinical <- as.factor(trimws(d[,"gleasonpatternsclinical"]))
# THIS CODING NEEDS TO BE CONFIRMED
levels(gleasonpatternsclinical) <- list(Primary_1_secondary_1.11="11",
Primary_1_secondary_3.13="13",
Primary_2_secondary_3.23="23",
Primary_2_secondary_5.25="25",
Primary_3_secondary_3.33="33",
Primary_3_secondary_5.34="34",
Primary_3_secondary_5.35="35",
Primary_3_secondary_unknown.39="39",
Primary_4_secondary_3.43="43",
Primary_4_secondary_4.44="44",
Primary_4_secondary_5.45="45",
Primary_5_secondary_3.53="53",
Primary_5_secondary_4.54="54",
Primary_5_secondary_5.55="55",
No_needle_core_biopsy_TURP_performed.X7 = "X7",
Not_documented.X9 = "X9"
)
new.d <- data.frame(new.d, gleasonpatternsclinical)
new.d <- apply_labels(new.d, gleasonpatternsclinical = "Gleason primary and secondary patterns ")
#summary(new.d$gleasonpatternsclinical)
temp.d <- data.frame (new.d.1, gleasonpatternsclinical)
summarytools::view(dfSummary(new.d$gleasonpatternsclinical, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleasonpatternsclinical
[labelled, factor] |
Gleason primary and secondary patterns |
1. Primary_1_secondary_1.11
2. Primary_1_secondary_3.13
3. Primary_2_secondary_3.23
4. Primary_2_secondary_5.25
5. Primary_3_secondary_3.33
6. Primary_3_secondary_5.34
7. Primary_3_secondary_5.35
8. Primary_3_secondary_unkno
9. Primary_4_secondary_3.43
10. Primary_4_secondary_4.44
11. Primary_4_secondary_5.45
12. Primary_5_secondary_3.53
13. Primary_5_secondary_4.54
14. Primary_5_secondary_5.55
15. No_needle_core_biopsy_TUR
16. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 12.1% | ) | | 10 | ( | 30.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 36.4% | ) | | 2 | ( | 6.1% | ) | | 3 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.0% | ) | | 1 | ( | 3.0% | ) |
|
 |
2134
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_clinical
[factor] |
1. Primary_1_secondary_1.11
2. Primary_1_secondary_3.13
3. Primary_2_secondary_3.23
4. Primary_2_secondary_5.25
5. Primary_3_secondary_3.33
6. Primary_3_secondary_5.34
7. Primary_3_secondary_5.35
8. Primary_3_secondary_unknown.39
9. Primary_4_secondary_3.43
10. Primary_4_secondary_4.44
11. Primary_4_secondary_5.45
12. Primary_5_secondary_3.53
13. Primary_5_secondary_4.54
14. Primary_5_secondary_5.55
15. No_needle_core_biopsy_TURP_performed.X7
16. Not_documented.X9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_clinical
[factor] |
1. Primary_1_secondary_1.11
2. Primary_1_secondary_3.13
3. Primary_2_secondary_3.23
4. Primary_2_secondary_5.25
5. Primary_3_secondary_3.33
6. Primary_3_secondary_5.34
7. Primary_3_secondary_5.35
8. Primary_3_secondary_unknown.39
9. Primary_4_secondary_3.43
10. Primary_4_secondary_4.44
11. Primary_4_secondary_5.45
12. Primary_5_secondary_3.53
13. Primary_5_secondary_4.54
14. Primary_5_secondary_5.55
15. No_needle_core_biopsy_TURP_performed.X7
16. Not_documented.X9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_clinical
[factor] |
1. Primary_1_secondary_1.11
2. Primary_1_secondary_3.13
3. Primary_2_secondary_3.23
4. Primary_2_secondary_5.25
5. Primary_3_secondary_3.33
6. Primary_3_secondary_5.34
7. Primary_3_secondary_5.35
8. Primary_3_secondary_unknown.39
9. Primary_4_secondary_3.43
10. Primary_4_secondary_4.44
11. Primary_4_secondary_5.45
12. Primary_5_secondary_3.53
13. Primary_5_secondary_4.54
14. Primary_5_secondary_5.55
15. No_needle_core_biopsy_TURP_performed.X7
16. Not_documented.X9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_clinical
[factor] |
1. Primary_1_secondary_1.11
2. Primary_1_secondary_3.13
3. Primary_2_secondary_3.23
4. Primary_2_secondary_5.25
5. Primary_3_secondary_3.33
6. Primary_3_secondary_5.34
7. Primary_3_secondary_5.35
8. Primary_3_secondary_unkno
9. Primary_4_secondary_3.43
10. Primary_4_secondary_4.44
11. Primary_4_secondary_5.45
12. Primary_5_secondary_3.53
13. Primary_5_secondary_4.54
14. Primary_5_secondary_5.55
15. No_needle_core_biopsy_TUR
16. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 9.4% | ) | | 10 | ( | 31.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 37.5% | ) | | 2 | ( | 6.2% | ) | | 3 | ( | 9.4% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.1% | ) | | 1 | ( | 3.1% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_clinical
[factor] |
1. Primary_1_secondary_1.11
2. Primary_1_secondary_3.13
3. Primary_2_secondary_3.23
4. Primary_2_secondary_5.25
5. Primary_3_secondary_3.33
6. Primary_3_secondary_5.34
7. Primary_3_secondary_5.35
8. Primary_3_secondary_unknown.39
9. Primary_4_secondary_3.43
10. Primary_4_secondary_4.44
11. Primary_4_secondary_5.45
12. Primary_5_secondary_3.53
13. Primary_5_secondary_4.54
14. Primary_5_secondary_5.55
15. No_needle_core_biopsy_TURP_performed.X7
16. Not_documented.X9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_clinical
[factor] |
1. Primary_1_secondary_1.11
2. Primary_1_secondary_3.13
3. Primary_2_secondary_3.23
4. Primary_2_secondary_5.25
5. Primary_3_secondary_3.33
6. Primary_3_secondary_5.34
7. Primary_3_secondary_5.35
8. Primary_3_secondary_unkno
9. Primary_4_secondary_3.43
10. Primary_4_secondary_4.44
11. Primary_4_secondary_5.45
12. Primary_5_secondary_3.53
13. Primary_5_secondary_4.54
14. Primary_5_secondary_5.55
15. No_needle_core_biopsy_TUR
16. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_clinical
[factor] |
1. Primary_1_secondary_1.11
2. Primary_1_secondary_3.13
3. Primary_2_secondary_3.23
4. Primary_2_secondary_5.25
5. Primary_3_secondary_3.33
6. Primary_3_secondary_5.34
7. Primary_3_secondary_5.35
8. Primary_3_secondary_unknown.39
9. Primary_4_secondary_3.43
10. Primary_4_secondary_4.44
11. Primary_4_secondary_5.45
12. Primary_5_secondary_3.53
13. Primary_5_secondary_4.54
14. Primary_5_secondary_5.55
15. No_needle_core_biopsy_TURP_performed.X7
16. Not_documented.X9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
GLEASON PATTERNS PATHOLOGICAL
Description: Prostate cancers are graded using Gleason score or pattern. This data item represents the Gleason primary and secondary patterns from prostatectomy or autopsy.
Rationale: Gleason Patterns Pathological is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 9.
Codes
- 14 Primary pattern 1, secondary pattern 4
- 15 Primary pattern 1, secondary pattern 5
- 33 Primary pattern 3, secondary pattern 3
- 34 Primary pattern 3, secondary pattern 4
- 35 Primary pattern 3, secondary pattern 5
- 41 Primary pattern 4, secondary pattern 1
- 43 Primary pattern 4, secondary pattern 3
- 44 Primary pattern 4, secondary pattern 4
- 45 Primary pattern 4, secondary pattern 5
- 53 Primary pattern 5, secondary pattern 3
- 54 Primary pattern 5, secondary pattern 4
- X6 Prostatectomy done, primary pattern unknown, secondary pattern unknown
- X7 No prostatectomy/autopsy performed
- X9 Not documented in medical record/Gleason Patterns Pathological not assessed or unknown if assessed Unknown if prostatectomy done
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3839
All data
st_css() #IMPORTANT!
gleasonpatternspathological <- as.factor(trimws(d[,"gleasonpatternspathological"]))
levels(gleasonpatternspathological) <- list(Primary_1_secondary_4.14="14",
Primary_1_secondary_5.15="15",
Primary_3_secondary_3.33="33",
Primary_3_secondary_5.34="34",
Primary_3_secondary_5.35="35",
Primary_4_secondary_1.41="41",
Primary_4_secondary_3.43="43",
Primary_4_secondary_4.44="44",
Primary_4_secondary_5.45="45",
Primary_5_secondary_3.53="53",
Primary_5_secondary_4.54="54",
Primary_unknown_secondary_unknown.X6="X6",
No_prostatectomy_autopsy_performed.X6= "X6",
Not_documented.X9 = "X9"
)
new.d <- data.frame(new.d, gleasonpatternspathological)
new.d <- apply_labels(new.d, gleasonpatternspathological = "Gleason primary and secondary patterns ")
#summary(new.d$gleasonpatternspathological)
temp.d <- data.frame (new.d.1, gleasonpatternspathological)
summarytools::view(dfSummary(new.d$gleasonpatternspathological, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleasonpatternspathological
[labelled, factor] |
Gleason primary and secondary patterns |
1. Primary_1_secondary_4.14
2. Primary_1_secondary_5.15
3. Primary_3_secondary_3.33
4. Primary_3_secondary_5.34
5. Primary_3_secondary_5.35
6. Primary_4_secondary_1.41
7. Primary_4_secondary_3.43
8. Primary_4_secondary_4.44
9. Primary_4_secondary_5.45
10. Primary_5_secondary_3.53
11. Primary_5_secondary_4.54
12. Primary_unknown_secondary
13. No_prostatectomy_autopsy_
14. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 2 | ( | 28.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 28.6% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
2160
(99.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_pathologica
[factor] |
1. Primary_1_secondary_4.14
2. Primary_1_secondary_5.15
3. Primary_3_secondary_3.33
4. Primary_3_secondary_5.34
5. Primary_3_secondary_5.35
6. Primary_4_secondary_1.41
7. Primary_4_secondary_3.43
8. Primary_4_secondary_4.44
9. Primary_4_secondary_5.45
10. Primary_5_secondary_3.53
11. Primary_5_secondary_4.54
12. Primary_unknown_secondary_unknown.X6
13. No_prostatectomy_autopsy_performed.X6
14. Not_documented.X9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_pathologica
[factor] |
1. Primary_1_secondary_4.14
2. Primary_1_secondary_5.15
3. Primary_3_secondary_3.33
4. Primary_3_secondary_5.34
5. Primary_3_secondary_5.35
6. Primary_4_secondary_1.41
7. Primary_4_secondary_3.43
8. Primary_4_secondary_4.44
9. Primary_4_secondary_5.45
10. Primary_5_secondary_3.53
11. Primary_5_secondary_4.54
12. Primary_unknown_secondary_unknown.X6
13. No_prostatectomy_autopsy_performed.X6
14. Not_documented.X9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_pathologica
[factor] |
1. Primary_1_secondary_4.14
2. Primary_1_secondary_5.15
3. Primary_3_secondary_3.33
4. Primary_3_secondary_5.34
5. Primary_3_secondary_5.35
6. Primary_4_secondary_1.41
7. Primary_4_secondary_3.43
8. Primary_4_secondary_4.44
9. Primary_4_secondary_5.45
10. Primary_5_secondary_3.53
11. Primary_5_secondary_4.54
12. Primary_unknown_secondary_unknown.X6
13. No_prostatectomy_autopsy_performed.X6
14. Not_documented.X9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_pathologica
[factor] |
1. Primary_1_secondary_4.14
2. Primary_1_secondary_5.15
3. Primary_3_secondary_3.33
4. Primary_3_secondary_5.34
5. Primary_3_secondary_5.35
6. Primary_4_secondary_1.41
7. Primary_4_secondary_3.43
8. Primary_4_secondary_4.44
9. Primary_4_secondary_5.45
10. Primary_5_secondary_3.53
11. Primary_5_secondary_4.54
12. Primary_unknown_secondary
13. No_prostatectomy_autopsy_
14. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 14.3% | ) | | 2 | ( | 28.6% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 28.6% | ) | | 1 | ( | 14.3% | ) | | 1 | ( | 14.3% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
167
(96.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_pathologica
[factor] |
1. Primary_1_secondary_4.14
2. Primary_1_secondary_5.15
3. Primary_3_secondary_3.33
4. Primary_3_secondary_5.34
5. Primary_3_secondary_5.35
6. Primary_4_secondary_1.41
7. Primary_4_secondary_3.43
8. Primary_4_secondary_4.44
9. Primary_4_secondary_5.45
10. Primary_5_secondary_3.53
11. Primary_5_secondary_4.54
12. Primary_unknown_secondary_unknown.X6
13. No_prostatectomy_autopsy_performed.X6
14. Not_documented.X9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_pathologica
[factor] |
1. Primary_1_secondary_4.14
2. Primary_1_secondary_5.15
3. Primary_3_secondary_3.33
4. Primary_3_secondary_5.34
5. Primary_3_secondary_5.35
6. Primary_4_secondary_1.41
7. Primary_4_secondary_3.43
8. Primary_4_secondary_4.44
9. Primary_4_secondary_5.45
10. Primary_5_secondary_3.53
11. Primary_5_secondary_4.54
12. Primary_unknown_secondary_unknown.X6
13. No_prostatectomy_autopsy_performed.X6
14. Not_documented.X9 |
All NA's |
|
1087
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_patterns_pathologica
[factor] |
1. Primary_1_secondary_4.14
2. Primary_1_secondary_5.15
3. Primary_3_secondary_3.33
4. Primary_3_secondary_5.34
5. Primary_3_secondary_5.35
6. Primary_4_secondary_1.41
7. Primary_4_secondary_3.43
8. Primary_4_secondary_4.44
9. Primary_4_secondary_5.45
10. Primary_5_secondary_3.53
11. Primary_5_secondary_4.54
12. Primary_unknown_secondary_unknown.X6
13. No_prostatectomy_autopsy_performed.X6
14. Not_documented.X9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
GLEASON SCORE CLINICAL
Description: This data item records the Gleason score based on adding the values for primary and secondary patterns in Needle Core Biopsy or TURP.
Rationale: Gleason Score Clinical is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 8.
Codes
- 02 Gleason score 2
- 03 Gleason score 3
- 04 Gleason score 4
- 05 Gleason score 5
- 06 Gleason score 6
- 07 Gleason score 7
- 08 Gleason score 8
- 09 Gleason score 9
- 10 Gleason score 10
- X7 No needle core biopsy/TURP performed
- X8 Not applicable: Information not collected for this case (If this information is required by your standard setter, use of code X8 may result in an edit error.)
- X9 Not documented in medical record/Gleason Score Clinical not assessed or unknown if assessed
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3840
All data
st_css() #IMPORTANT!
gleasonscoreclinical <- trimws(d[,"gleasonscoreclinical"])
gleasonscoreclinical[ which(gleasonscoreclinical=="06")]<-"6"
gleasonscoreclinical[ which(gleasonscoreclinical=="07")]<-"7"
gleasonscoreclinical[ which(gleasonscoreclinical=="08")]<-"8"
gleasonscoreclinical[ which(gleasonscoreclinical=="09")]<-"9"
gleasonscoreclinical<-as.factor(gleasonscoreclinical)
levels(gleasonscoreclinical) <- list(Gleason_score_2.2="2",
Gleason_score_3.3="3",
Gleason_score_4.4="4",
Gleason_score_5.5="5",
Gleason_score_6.6="6",
Gleason_score_7.7="7",
Gleason_score_8.8="8",
Gleason_score_9.9="9",
Gleason_score_10.10="10",
Not_documented.X9 = "X9")
new.d <- data.frame(new.d, gleasonscoreclinical)
new.d <- apply_labels(new.d, gleasonscoreclinical = " Gleason score")
#summary(new.d$gleasonscoreclinical)
temp.d <- data.frame (new.d.1, gleasonscoreclinical)
summarytools::view(dfSummary(new.d$gleasonscoreclinical, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE , headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleasonscoreclinical
[labelled, factor] |
Gleason score |
1. Gleason_score_2.2
2. Gleason_score_3.3
3. Gleason_score_4.4
4. Gleason_score_5.5
5. Gleason_score_6.6
6. Gleason_score_7.7
7. Gleason_score_8.8
8. Gleason_score_9.9
9. Gleason_score_10.10
10. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 12.1% | ) | | 20 | ( | 60.6% | ) | | 1 | ( | 3.0% | ) | | 2 | ( | 6.1% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 18.2% | ) |
|
 |
2134
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_clinical
[factor] |
1. Gleason_score_2.2
2. Gleason_score_3.3
3. Gleason_score_4.4
4. Gleason_score_5.5
5. Gleason_score_6.6
6. Gleason_score_7.7
7. Gleason_score_8.8
8. Gleason_score_9.9
9. Gleason_score_10.10
10. Not_documented.X9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_clinical
[factor] |
1. Gleason_score_2.2
2. Gleason_score_3.3
3. Gleason_score_4.4
4. Gleason_score_5.5
5. Gleason_score_6.6
6. Gleason_score_7.7
7. Gleason_score_8.8
8. Gleason_score_9.9
9. Gleason_score_10.10
10. Not_documented.X9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_clinical
[factor] |
1. Gleason_score_2.2
2. Gleason_score_3.3
3. Gleason_score_4.4
4. Gleason_score_5.5
5. Gleason_score_6.6
6. Gleason_score_7.7
7. Gleason_score_8.8
8. Gleason_score_9.9
9. Gleason_score_10.10
10. Not_documented.X9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_clinical
[factor] |
1. Gleason_score_2.2
2. Gleason_score_3.3
3. Gleason_score_4.4
4. Gleason_score_5.5
5. Gleason_score_6.6
6. Gleason_score_7.7
7. Gleason_score_8.8
8. Gleason_score_9.9
9. Gleason_score_10.10
10. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 9.4% | ) | | 20 | ( | 62.5% | ) | | 1 | ( | 3.1% | ) | | 2 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 18.8% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_clinical
[factor] |
1. Gleason_score_2.2
2. Gleason_score_3.3
3. Gleason_score_4.4
4. Gleason_score_5.5
5. Gleason_score_6.6
6. Gleason_score_7.7
7. Gleason_score_8.8
8. Gleason_score_9.9
9. Gleason_score_10.10
10. Not_documented.X9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_clinical
[factor] |
1. Gleason_score_2.2
2. Gleason_score_3.3
3. Gleason_score_4.4
4. Gleason_score_5.5
5. Gleason_score_6.6
6. Gleason_score_7.7
7. Gleason_score_8.8
8. Gleason_score_9.9
9. Gleason_score_10.10
10. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_clinical
[factor] |
1. Gleason_score_2.2
2. Gleason_score_3.3
3. Gleason_score_4.4
4. Gleason_score_5.5
5. Gleason_score_6.6
6. Gleason_score_7.7
7. Gleason_score_8.8
8. Gleason_score_9.9
9. Gleason_score_10.10
10. Not_documented.X9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
GLEASON SCORE PATHOLOGICAL
Description: This data item records the Gleason score based on adding the values for primary and secondary patterns from prostatectomy or autopsy.
Rationale: Gleason Score Pathological is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 10.
Codes
- 02 Gleason score 2
- 03 Gleason score 3
- 04 Gleason score 4
- 05 Gleason score 5
- 06 Gleason score 6
- 07 Gleason score 7
- 08 Gleason score 8
- 09 Gleason score 9
- 10 Gleason score 10
- X7 No prostatectomy done
- X8 Not applicable: Information not collected for this case (If this information is required by your standard setter, use of code X8 may result in an edit error.)
- X9 Not documented in medical record/Gleason Score Pathological not assessed or unknown if assessed
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3841
All data
st_css() #IMPORTANT!
gleasonscorepathological <- as.factor(trimws(d[,"gleasonscorepathological"]))
levels(gleasonscorepathological) <- list(Gleason_score_3.03 ="03",
Gleason_score_4.04="04",
Gleason_score_6.06="06",
Gleason_score_7.07="07",
Gleason_score_8.08="08",
Gleason_score_9.09="09",
No_prostatectomy_done.X7="X7",
Not_applicable.X8="X8",
Not_documented.X9 = "X9")
new.d <- data.frame(new.d, gleasonscorepathological)
new.d <- apply_labels(new.d, gleasonscorepathological = " Gleason score")
#summary(new.d$gleasonscorepathological)
temp.d <- data.frame (new.d.1, gleasonscorepathological)
summarytools::view(dfSummary(new.d$gleasonscorepathological, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleasonscorepathological
[labelled, factor] |
Gleason score |
1. Gleason_score_3.03
2. Gleason_score_4.04
3. Gleason_score_6.06
4. Gleason_score_7.07
5. Gleason_score_8.08
6. Gleason_score_9.09
7. No_prostatectomy_done.X7
8. Not_applicable.X8
9. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.0% | ) | | 4 | ( | 12.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.0% | ) | | 21 | ( | 63.6% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 18.2% | ) |
|
 |
2134
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_pathological
[factor] |
1. Gleason_score_3.03
2. Gleason_score_4.04
3. Gleason_score_6.06
4. Gleason_score_7.07
5. Gleason_score_8.08
6. Gleason_score_9.09
7. No_prostatectomy_done.X7
8. Not_applicable.X8
9. Not_documented.X9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_pathological
[factor] |
1. Gleason_score_3.03
2. Gleason_score_4.04
3. Gleason_score_6.06
4. Gleason_score_7.07
5. Gleason_score_8.08
6. Gleason_score_9.09
7. No_prostatectomy_done.X7
8. Not_applicable.X8
9. Not_documented.X9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_pathological
[factor] |
1. Gleason_score_3.03
2. Gleason_score_4.04
3. Gleason_score_6.06
4. Gleason_score_7.07
5. Gleason_score_8.08
6. Gleason_score_9.09
7. No_prostatectomy_done.X7
8. Not_applicable.X8
9. Not_documented.X9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_pathological
[factor] |
1. Gleason_score_3.03
2. Gleason_score_4.04
3. Gleason_score_6.06
4. Gleason_score_7.07
5. Gleason_score_8.08
6. Gleason_score_9.09
7. No_prostatectomy_done.X7
8. Not_applicable.X8
9. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.1% | ) | | 4 | ( | 12.5% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.1% | ) | | 20 | ( | 62.5% | ) | | 0 | ( | 0.0% | ) | | 6 | ( | 18.8% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_pathological
[factor] |
1. Gleason_score_3.03
2. Gleason_score_4.04
3. Gleason_score_6.06
4. Gleason_score_7.07
5. Gleason_score_8.08
6. Gleason_score_9.09
7. No_prostatectomy_done.X7
8. Not_applicable.X8
9. Not_documented.X9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_pathological
[factor] |
1. Gleason_score_3.03
2. Gleason_score_4.04
3. Gleason_score_6.06
4. Gleason_score_7.07
5. Gleason_score_8.08
6. Gleason_score_9.09
7. No_prostatectomy_done.X7
8. Not_applicable.X8
9. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_score_pathological
[factor] |
1. Gleason_score_3.03
2. Gleason_score_4.04
3. Gleason_score_6.06
4. Gleason_score_7.07
5. Gleason_score_8.08
6. Gleason_score_9.09
7. No_prostatectomy_done.X7
8. Not_applicable.X8
9. Not_documented.X9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
GLEASON TERTIARY PATTERN
Description: Prostate cancers are graded using Gleason score or pattern. This data item represents the tertiary pattern value from prostatectomy or autopsy.
Rationale: Tertiary Gleason pattern on prostatectomy is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 11.
Codes
- 10 Tertiary pattern 1
- 20 Tertiary pattern 2
- 30 Tertiary pattern 3
- 40 Tertiary pattern 4
- 50 Tertiary pattern 5
- X7 No prostatectomy/autopsy performed
- X8 Not applicable: Information not collected for this case (If this information is required by your standard setter, use of code X8 may result in an edit error.)
- X9 Not documented in medical record/Gleason Tertiary Pattern not assessed or unknown if assessed
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3842
All data
st_css() #IMPORTANT!
gleasontertiarypattern <- as.factor(trimws(d[,"gleasontertiarypattern"]))
levels(gleasontertiarypattern) <- list(Tertiary_pattern_2.20="20",
Tertiary_pattern_3.30="30",
Tertiary_pattern_4.40="40",
Tertiary_pattern_5.50="50",
No_prostatectomy_autopsy_performed.X7="X7",
Not_applicable.X8="X8",
Not_documented.X9 = "X9")
new.d <- data.frame(new.d, gleasontertiarypattern)
new.d <- apply_labels(new.d, gleasontertiarypattern = " Gleason score")
#summary(new.d$gleasontertiarypattern)
temp.d <- data.frame (new.d.1, gleasontertiarypattern)
summarytools::view(dfSummary(new.d$gleasontertiarypattern, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleasontertiarypattern
[labelled, factor] |
Gleason score |
1. Tertiary_pattern_2.20
2. Tertiary_pattern_3.30
3. Tertiary_pattern_4.40
4. Tertiary_pattern_5.50
5. No_prostatectomy_autopsy_
6. Not_applicable.X8
7. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 21 | ( | 63.6% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 36.4% | ) |
|
 |
2134
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_tertiary_pattern
[factor] |
1. Tertiary_pattern_2.20
2. Tertiary_pattern_3.30
3. Tertiary_pattern_4.40
4. Tertiary_pattern_5.50
5. No_prostatectomy_autopsy_performed.X7
6. Not_applicable.X8
7. Not_documented.X9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_tertiary_pattern
[factor] |
1. Tertiary_pattern_2.20
2. Tertiary_pattern_3.30
3. Tertiary_pattern_4.40
4. Tertiary_pattern_5.50
5. No_prostatectomy_autopsy_performed.X7
6. Not_applicable.X8
7. Not_documented.X9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_tertiary_pattern
[factor] |
1. Tertiary_pattern_2.20
2. Tertiary_pattern_3.30
3. Tertiary_pattern_4.40
4. Tertiary_pattern_5.50
5. No_prostatectomy_autopsy_performed.X7
6. Not_applicable.X8
7. Not_documented.X9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_tertiary_pattern
[factor] |
1. Tertiary_pattern_2.20
2. Tertiary_pattern_3.30
3. Tertiary_pattern_4.40
4. Tertiary_pattern_5.50
5. No_prostatectomy_autopsy_
6. Not_applicable.X8
7. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 20 | ( | 62.5% | ) | | 0 | ( | 0.0% | ) | | 12 | ( | 37.5% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_tertiary_pattern
[factor] |
1. Tertiary_pattern_2.20
2. Tertiary_pattern_3.30
3. Tertiary_pattern_4.40
4. Tertiary_pattern_5.50
5. No_prostatectomy_autopsy_performed.X7
6. Not_applicable.X8
7. Not_documented.X9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_tertiary_pattern
[factor] |
1. Tertiary_pattern_2.20
2. Tertiary_pattern_3.30
3. Tertiary_pattern_4.40
4. Tertiary_pattern_5.50
5. No_prostatectomy_autopsy_
6. Not_applicable.X8
7. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gleason_tertiary_pattern
[factor] |
1. Tertiary_pattern_2.20
2. Tertiary_pattern_3.30
3. Tertiary_pattern_4.40
4. Tertiary_pattern_5.50
5. No_prostatectomy_autopsy_performed.X7
6. Not_applicable.X8
7. Not_documented.X9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
GRADE CLINICAL
Description: This data item records the grade of a solid primary tumor before any treatment (surgical resection or initiation of any treatment including neoadjuvant). For cases diagnosed January 1, 2018, and later, this data item, along with Grade Pathological and Grade Post-Neoadjuvant, replaces NAACCR Data Item Grade [440] as well as SSF’s for cancer sites with alternative grading systems (e.g., breast [Bloom-Richardson], prostate [Gleason]).
Rationale: Grade is a measure of the aggressiveness of the tumor. Grade and cell type are important prognostic indicators for many cancers. For some sites, grade is required to assign the clinical stage group. For those cases that are eligible AJCC staging, the recommended grading system is specified in the AJCC Chapter. The AJCC Chapter-specific grading systems (codes 1-5) take priority over the generic grade definitions (codes A-E, L, H, 9). For those cases that are not eligible for AJCC staging, if the recommended grading system is not documented, the generic grade definitions would apply.
Codes
- 1 Grade Group 1: Gleason score less than or equal to 6
- 2 Grade Group 2: Gleason score 7/Gleason pattern 3+4
- 3 Grade Group 3: Gleason score 7/Gleason pattern 4+3
- 4 Grade Group 4: Gleason score 8
- 5 Grade Group 5: Gleason score 9 or 10
- 9 Grade cannot be assessed; Unknown
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank. Leave blank for cases diagnosed prior to 2018.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3843
All data
st_css() #IMPORTANT!
gradeclinical <- as.factor(trimws(d[,"gradeclinical"]))
levels(gradeclinical) <- list(Grade_Group_1.1="1",
Grade_Group_2.2="2",
Grade_Group_3.3="3",
Grade_Group_4.4="4",
Grade_Group_5.5="5",
Not_found_grade_8.8="8",
Unknown.9 = "9")
new.d <- data.frame(new.d, gradeclinical)
new.d <- apply_labels(new.d, gradeclinical = "Grade of primary tumor before any treatment")
#summary(new.d$gradeclinical)
temp.d <- data.frame (new.d.1, gradeclinical)
summarytools::view(dfSummary(new.d$gradeclinical, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE , headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gradeclinical
[labelled, factor] |
Grade of primary tumor before any
treatment |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
| 4 | ( | 11.8% | ) | | 10 | ( | 29.4% | ) | | 11 | ( | 32.4% | ) | | 3 | ( | 8.8% | ) | | 3 | ( | 8.8% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 8.8% | ) |
|
 |
2133
(98.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_clinical
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_clinical
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_clinical
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
| 3 | ( | 9.4% | ) | | 10 | ( | 31.2% | ) | | 11 | ( | 34.4% | ) | | 3 | ( | 9.4% | ) | | 3 | ( | 9.4% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.2% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_clinical
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_clinical
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
| 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_clinical
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
GRADE PATHOLOGICAL
Description: This data item records the grade of a solid primary tumor that has been resected and for which no neoadjuvant therapy was administered. If AJCC staging is being assigned, the tumor must have met the surgical resection requirements in the AJCC manual. This may include the grade from the clinical workup. Record the highest grade documented from any microscopic specimen of the primary site whether from the clinical workup or the surgical resection. For cases diagnosed January 1, 2018, and later, this data item, along with Grade Clinical and Grade Post-Neoadjuvant, replaces NAACCR Data Item Grade [440] as well as SSF’s for cancer sites with alternative grading systems (e.g., breast [Bloom-Richardson], prostate [Gleason]).
Rationale: Grade is a measure of the aggressiveness of the tumor. Grade and cell type are important prognostic indicators for many cancers. For some sites, grade is required to assign the pathological stage group. For those cases that are eligible AJCC staging, the recommended grading system is specified in the AJCC Chapter. The AJCC Chapter-specific grading systems (codes 1-5) take priority over the generic grade definitions (codes A-E, L, H, 9). For those cases that are not eligible for AJCC staging, if the recommended grading system is not documented, the generic grade definitions would apply.
Codes
- 1 Grade Group 1: Gleason score less than or equal to 6
- 2 Grade Group 2: Gleason score 7/Gleason pattern 3+4
- 3 Grade Group 3: Gleason score 7/Gleason pattern 4+3
- 4 Grade Group 4: Gleason score 8
- 5 Grade Group 5: Gleason score 9 or 10
- 9 Grade cannot be assessed; Unknown
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank. Leave blank for cases diagnosed prior to 2018.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3844
All data
st_css() #IMPORTANT!
gradepathological <- as.factor(trimws(d[,"gradepathological"]))
levels(gradepathological) <- list(Grade_Group_1.1="1",
Grade_Group_2.2="2",
Grade_Group_3.3="3",
Grade_Group_4.4="4",
Grade_Group_5.5="5",
Not_found_grade_8.8="8",
Unknown.9 = "9")
new.d <- data.frame(new.d, gradepathological)
new.d <- apply_labels(new.d, gradepathological = "Grade of primary tumor before any treatment")
#summary(new.d$gradepathological)
temp.d <- data.frame (new.d.1, gradepathological)
summarytools::view(dfSummary(new.d$gradepathological, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
gradepathological
[labelled, factor] |
Grade of primary tumor before any
treatment |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
| 1 | ( | 2.9% | ) | | 2 | ( | 5.9% | ) | | 3 | ( | 8.8% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 5.9% | ) | | 0 | ( | 0.0% | ) | | 26 | ( | 76.5% | ) |
|
 |
2133
(98.4%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_pathological
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_pathological
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_pathological
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
| 1 | ( | 3.1% | ) | | 2 | ( | 6.2% | ) | | 2 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 25 | ( | 78.1% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_pathological
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_pathological
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 50.0% | ) |
|
 |
1085
(99.8%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
grade_pathological
[factor] |
1. Grade_Group_1.1
2. Grade_Group_2.2
3. Grade_Group_3.3
4. Grade_Group_4.4
5. Grade_Group_5.5
6. Not_found_grade_8.8
7. Unknown.9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
NUMBER OF CORES EXAMINED
Description: This data item represents the number of cores examined as documented in the pathology report from needle biopsy of the prostate gland.
Rationale: Number of Cores Examined is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 13.
Codes:
- 01-99 1 - 99 cores examined(Exact number of cores examined)
- X1 100 or more cores examined
- X6 Biopsy cores examined, number unknown
- X7 No needle core biopsy performed
- X8 Not applicable: Information not collected for this case(If this information is required by your standard setter, use of code X8 may result in an edit error.)
- X9 Not documented in medical record/Number of cores examined not assessed or unknown if assessed
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3897
All data
st_css() #IMPORTANT!
numberofcoresexamined <- trimws(d[,"numberofcoresexamined"])
numberofcoresexamined[ which(numberofcoresexamined=="09")]<-"9"
numberofcoresexamined[ which(numberofcoresexamined=="08")]<-"8"
numberofcoresexamined<-as.factor(numberofcoresexamined)
levels(numberofcoresexamined) <- list(examined_1_core.1="1",
examined_2_core.2="2",
examined_3_core.3="3",
examined_4_core.4="4",
examined_5_core.5="5",
examined_6_core.6="6",
examined_7_core.7="7",
examined_8_core.8="8",
examined_9_core.9="9",
examined_10_core.10="10",
examined_11_core.11="11",
examined_12_core.12="12",
examined_13_core.13="13",
examined_14_core.14="14",
examined_15_core.15="15",
examined_16_core.16="16",
examined_17_core.17="17",
examined_18_core.18="18",
examined_19_core.19="19",
examined_20_core.20="20",
examined_22_core.22="22",
examined_21_core.21="21",
examined_23_core.23="23",
examined_24_core.24="24",
examined_25_core.25="25",
examined_27_core.27="27",
examined_29_core.29="29",
examined_30_core.30="30",
examined_31_core.31="31",
examined_35_core.35="35",
examined_36_core.36="36",
examined_45_core.45="45",
examined_91_core.91="91",
examined_99_core.99="99",
Biopsy_cores_examined_unknown_number.X6="X6",
No_needle_core_biopsy_performed.X7 = "X7",
Not_documented.X9 = "X9")
new.d <- data.frame(new.d, numberofcoresexamined)
new.d <- apply_labels(new.d, numberofcoresexamined = " Gleason score")
#summary(new.d$numberofcoresexamined)
temp.d <- data.frame (new.d.1, numberofcoresexamined)
summarytools::view(dfSummary(new.d$numberofcoresexamined, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
numberofcoresexamined
[labelled, factor] |
Gleason score |
1. examined_1_core.1
2. examined_2_core.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unk
36. No_needle_core_biopsy_per
37. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 19 | ( | 57.6% | ) | | 2 | ( | 6.1% | ) | | 1 | ( | 3.0% | ) | | 1 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 12.1% | ) | | 2 | ( | 6.1% | ) | | 1 | ( | 3.0% | ) |
|
 |
2134
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_examined
[factor] |
1. examined_1_core.1
2. examined_2_core.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_documented.X9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_examined
[factor] |
1. examined_1_core.1
2. examined_2_core.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_documented.X9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_examined
[factor] |
1. examined_1_core.1
2. examined_2_core.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_documented.X9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_examined
[factor] |
1. examined_1_core.1
2. examined_2_core.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unk
36. No_needle_core_biopsy_per
37. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 18 | ( | 56.2% | ) | | 2 | ( | 6.2% | ) | | 1 | ( | 3.1% | ) | | 1 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 4 | ( | 12.5% | ) | | 2 | ( | 6.2% | ) | | 1 | ( | 3.1% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_examined
[factor] |
1. examined_1_core.1
2. examined_2_core.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_documented.X9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_examined
[factor] |
1. examined_1_core.1
2. examined_2_core.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unk
36. No_needle_core_biopsy_per
37. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_examined
[factor] |
1. examined_1_core.1
2. examined_2_core.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_documented.X9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
NUMBER OF CORES POSITIVE
Description:This data item represents the number of positive cores documented in the pathology report from needle biopsy of the prostate gland.
Rationale: Number of Cores Positive is a Registry Data Collection Variable for AJCC. This data item was previously collected as Prostate, CS SSF# 12.
Codes
- 00: All examined cores negative
- 01-99: 1 - 99 cores positive (Exact number of cores positive)
- X1: 100 or more cores positive
- X6: Biopsy cores positive, number unknown
- X7: No needle core biopsy performed
- X8: Not applicable: Information not collected for this case (If this information is required by your standard setter, use of code X8 may result in an edit error.)
- X9: Not documented in medical record/Number of Cores Positive not assessed or unknown if assessed
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.
Reference page: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3898
All data
st_css() #IMPORTANT!
numberofcorespositive <- trimws(d[,"numberofcorespositive"])
numberofcorespositive[ which(numberofcorespositive=="02")]<-"2"
numberofcorespositive[ which(numberofcorespositive=="04")]<-"4"
numberofcorespositive[ which(numberofcorespositive=="05")]<-"5"
numberofcorespositive[ which(numberofcorespositive=="01")]<-"1"
numberofcorespositive[ which(numberofcorespositive=="07")]<-"7"
numberofcorespositive[ which(numberofcorespositive=="09")]<-"9"
numberofcorespositive[ which(numberofcorespositive=="08")]<-"8"
numberofcorespositive[ which(numberofcorespositive=="03")]<-"3"
numberofcorespositive<-as.factor(numberofcorespositive)
levels(numberofcorespositive) <- list(examined_1_core.1="1",
examined_2_cores.2="2",
examined_3_core.3="3",
examined_4_core.4="4",
examined_5_core.5="5",
examined_6_core.6="6",
examined_7_core.7="7",
examined_8_core.8="8",
examined_9_core.9="9",
examined_10_core.10="10",
examined_11_core.11="11",
examined_12_core.12="12",
examined_13_core.13="13",
examined_14_core.14="14",
examined_15_core.15="15",
examined_16_core.16="16",
examined_17_core.17="17",
examined_18_core.18="18",
examined_19_core.19="19",
examined_20_core.20="20",
examined_22_core.22="22",
examined_21_core.21="21",
examined_23_core.23="23",
examined_24_core.24="24",
examined_25_core.25="25",
examined_27_core.27="27",
examined_29_core.29="29",
examined_30_core.30="30",
examined_31_core.31="31",
examined_35_core.35="35",
examined_36_core.36="36",
examined_45_core.45="45",
examined_91_core.91="91",
examined_99_core.99="99",
Biopsy_cores_examined_unknown_number.X6="X6",
No_needle_core_biopsy_performed.X7 = "X7",
Not_applicable.X8 = "X8",
Not_documented.X9 = "X9")
new.d <- data.frame(new.d, numberofcorespositive)
new.d <- apply_labels(new.d, numberofcorespositive = " Gleason score")
#summary(new.d$numberofcorespositive)
temp.d <- data.frame (new.d.1, numberofcorespositive)
summarytools::view(dfSummary(new.d$numberofcorespositive, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
numberofcorespositive
[labelled, factor] |
Gleason score |
1. examined_1_core.1
2. examined_2_cores.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unk
36. No_needle_core_biopsy_per
37. Not_applicable.X8
38. Not_documented.X9 |
| 3 | ( | 9.1% | ) | | 6 | ( | 18.2% | ) | | 2 | ( | 6.1% | ) | | 5 | ( | 15.2% | ) | | 7 | ( | 21.2% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.1% | ) | | 2 | ( | 6.1% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 9.1% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
2134
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_positive
[factor] |
1. examined_1_core.1
2. examined_2_cores.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_applicable.X8
38. Not_documented.X9 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_positive
[factor] |
1. examined_1_core.1
2. examined_2_cores.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_applicable.X8
38. Not_documented.X9 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_positive
[factor] |
1. examined_1_core.1
2. examined_2_cores.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_applicable.X8
38. Not_documented.X9 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_positive
[factor] |
1. examined_1_core.1
2. examined_2_cores.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unk
36. No_needle_core_biopsy_per
37. Not_applicable.X8
38. Not_documented.X9 |
| 3 | ( | 9.4% | ) | | 5 | ( | 15.6% | ) | | 2 | ( | 6.2% | ) | | 5 | ( | 15.6% | ) | | 7 | ( | 21.9% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.2% | ) | | 2 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 3 | ( | 9.4% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 3.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_positive
[factor] |
1. examined_1_core.1
2. examined_2_cores.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_applicable.X8
38. Not_documented.X9 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_positive
[factor] |
1. examined_1_core.1
2. examined_2_cores.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unk
36. No_needle_core_biopsy_per
37. Not_applicable.X8
38. Not_documented.X9 |
| 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
number_of_cores_positive
[factor] |
1. examined_1_core.1
2. examined_2_cores.2
3. examined_3_core.3
4. examined_4_core.4
5. examined_5_core.5
6. examined_6_core.6
7. examined_7_core.7
8. examined_8_core.8
9. examined_9_core.9
10. examined_10_core.10
11. examined_11_core.11
12. examined_12_core.12
13. examined_13_core.13
14. examined_14_core.14
15. examined_15_core.15
16. examined_16_core.16
17. examined_17_core.17
18. examined_18_core.18
19. examined_19_core.19
20. examined_20_core.20
21. examined_22_core.22
22. examined_21_core.21
23. examined_23_core.23
24. examined_24_core.24
25. examined_25_core.25
26. examined_27_core.27
27. examined_29_core.29
28. examined_30_core.30
29. examined_31_core.31
30. examined_35_core.35
31. examined_36_core.36
32. examined_45_core.45
33. examined_91_core.91
34. examined_99_core.99
35. Biopsy_cores_examined_unknown_number.X6
36. No_needle_core_biopsy_performed.X7
37. Not_applicable.X8
38. Not_documented.X9 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
EOD PROSTATE PATHOLOGIC EXTENSION
Description: EOD Prostate Pathologic Extension is used to assign pT category for prostate cancer based on radical prostatectomy specimens.
Rationale: EOD Prostate Pathologic Extension is used in EOD. It was previously collected as Prostate Pathological Extension, and Prostate, CS SSF# 3.
Codes (See the most current version of EOD (Prostate) (https://staging.seer.cancer.gov/) for rules and site-specific codes and coding structures.)
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3919
All data
st_css() #IMPORTANT!
prostatepathologicalextension <- as.factor(trimws(d[,"prostatepathologicalextension"]))
levels(prostatepathologicalextension) <- list(T2.300="300",
T4_600.600="600",
TX_900.900="900",
T3.500="500",
T3a.350="350",
T3b.400="400",
T4_700.700 = "700",
TX_950.950 = "950",
In_situ_88.0 = "0",
T0.800 = "800",
Not_found_250.250 = "250"
)
new.d <- data.frame(new.d, prostatepathologicalextension)
new.d <- apply_labels(new.d, prostatepathologicalextension = "Grade of primary tumor before any treatment")
#summary(new.d$prostatepathologicalextension)
temp.d <- data.frame (new.d.1, prostatepathologicalextension)
summarytools::view(dfSummary(new.d$prostatepathologicalextension, style = 'grid', max.distinct.values = 50, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
prostatepathologicalextension
[labelled, factor] |
Grade of primary tumor before any
treatment |
1. T2.300
2. T4_600.600
3. TX_900.900
4. T3.500
5. T3a.350
6. T3b.400
7. T4_700.700
8. TX_950.950
9. In_situ_88.0
10. T0.800
11. Not_found_250.250 |
| 5 | ( | 15.2% | ) | | 0 | ( | 0.0% | ) | | 26 | ( | 78.8% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.1% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
2134
(98.5%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
prostate_pathological_extension
[factor] |
1. T2.300
2. T4_600.600
3. TX_900.900
4. T3.500
5. T3a.350
6. T3b.400
7. T4_700.700
8. TX_950.950
9. In_situ_88.0
10. T0.800
11. Not_found_250.250 |
All NA's |
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
prostate_pathological_extension
[factor] |
1. T2.300
2. T4_600.600
3. TX_900.900
4. T3.500
5. T3a.350
6. T3b.400
7. T4_700.700
8. TX_950.950
9. In_situ_88.0
10. T0.800
11. Not_found_250.250 |
All NA's |
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
prostate_pathological_extension
[factor] |
1. T2.300
2. T4_600.600
3. TX_900.900
4. T3.500
5. T3a.350
6. T3b.400
7. T4_700.700
8. TX_950.950
9. In_situ_88.0
10. T0.800
11. Not_found_250.250 |
All NA's |
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
prostate_pathological_extension
[factor] |
1. T2.300
2. T4_600.600
3. TX_900.900
4. T3.500
5. T3a.350
6. T3b.400
7. T4_700.700
8. TX_950.950
9. In_situ_88.0
10. T0.800
11. Not_found_250.250 |
| 5 | ( | 15.6% | ) | | 0 | ( | 0.0% | ) | | 25 | ( | 78.1% | ) | | 0 | ( | 0.0% | ) | | 2 | ( | 6.2% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
142
(81.6%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
prostate_pathological_extension
[factor] |
1. T2.300
2. T4_600.600
3. TX_900.900
4. T3.500
5. T3a.350
6. T3b.400
7. T4_700.700
8. TX_950.950
9. In_situ_88.0
10. T0.800
11. Not_found_250.250 |
All NA's |
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
prostate_pathological_extension
[factor] |
1. T2.300
2. T4_600.600
3. TX_900.900
4. T3.500
5. T3a.350
6. T3b.400
7. T4_700.700
8. TX_950.950
9. In_situ_88.0
10. T0.800
11. Not_found_250.250 |
| 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 1 | ( | 100.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) | | 0 | ( | 0.0% | ) |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
prostate_pathological_extension
[factor] |
1. T2.300
2. T4_600.600
3. TX_900.900
4. T3.500
5. T3a.350
6. T3b.400
7. T4_700.700
8. TX_950.950
9. In_situ_88.0
10. T0.800
11. Not_found_250.250 |
All NA's |
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
PSA (PROSTATIC SPECIFIC ANTIGEN) LAB VALUE
Description: PSA (Prostatic Specific Antigen) is a protein produced by cells of the prostate gland and is elevated in patients with prostate cancer. This data item pertains to PSA lab value.
Rationale: This data item is required for prognostic stage grouping in AJCC 8th edition, Chapter 58, Prostate. It was previously collected as Prostate, CS SSF# 1.
Codes
- 0.1 0.1 or less nanograms/milliliter (ng/ml) (Exact value to nearest tenth of ng/ml)
- 0.2-999.9 0.2–999.9 ng/ml (Exact value to nearest tenth of ng/ml)
- XXX.1 1,000 ng/ml or greater
- XXX.7 Test ordered, results not in chart
- XXX.9 Not documented in medical record/PSA lab value not assessed or unknown if assessed
Each Site-Specific Data Item (SSDI) applies only to selected primary sites, histologies, and years of diagnosis. Depending on applicability and standard-setter requirements, SSDIs may be left blank.
Reference: http://datadictionary.naaccr.org/default.aspx?c=10&Version=18#3920
All data
st_css() #IMPORTANT!
psalabvalue <- trimws(d[,"psalabvalue"])
#psalabvalue[ which(psalabvalue=="XXX.1")]<-"10001"
#psalabvalue[ which(psalabvalue=="XXX.7")]<-"10007"
#psalabvalue[ which(psalabvalue=="XXX.9")]<-"10009"
psalabvalue<-as.factor(psalabvalue)
psalabvalue <- ifelse(psalabvalue=="XXX.9", NA,
ifelse(psalabvalue=="XXX.7", NA,
ifelse(psalabvalue=="XXX.1", 1000, psalabvalue)))
psalabvalue <- as.numeric(psalabvalue)
new.d <- data.frame(new.d, psalabvalue)
new.d <- apply_labels(new.d, psalabvalue = "PSA_lab_value")
#summary(new.d$psalabvalue)
temp.d <- data.frame (new.d.1, psalabvalue)
summarytools::view(dfSummary(new.d$psalabvalue, style = 'grid', max.distinct.values = 1000, plain.ascii = FALSE, valid.col = FALSE, headings = FALSE ), method = "render")
| No |
Variable |
Label |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
psalabvalue
[labelled, numeric] |
PSA_lab_value |
Mean (sd) : 47.7 (183.3)
min < med < max:
1 < 15 < 1000
IQR (CV) : 12 (3.8) |
| 1 | : | 1 | ( | 3.4% | ) | | 2 | : | 1 | ( | 3.4% | ) | | 3 | : | 1 | ( | 3.4% | ) | | 4 | : | 1 | ( | 3.4% | ) | | 5 | : | 1 | ( | 3.4% | ) | | 6 | : | 1 | ( | 3.4% | ) | | 7 | : | 1 | ( | 3.4% | ) | | 8 | : | 1 | ( | 3.4% | ) | | 9 | : | 1 | ( | 3.4% | ) | | 10 | : | 1 | ( | 3.4% | ) | | 11 | : | 1 | ( | 3.4% | ) | | 12 | : | 1 | ( | 3.4% | ) | | 13 | : | 1 | ( | 3.4% | ) | | 14 | : | 1 | ( | 3.4% | ) | | 15 | : | 2 | ( | 6.9% | ) | | 16 | : | 1 | ( | 3.4% | ) | | 17 | : | 2 | ( | 6.9% | ) | | 18 | : | 1 | ( | 3.4% | ) | | 19 | : | 1 | ( | 3.4% | ) | | 20 | : | 1 | ( | 3.4% | ) | | 21 | : | 1 | ( | 3.4% | ) | | 22 | : | 1 | ( | 3.4% | ) | | 23 | : | 1 | ( | 3.4% | ) | | 24 | : | 1 | ( | 3.4% | ) | | 25 | : | 1 | ( | 3.4% | ) | | 26 | : | 1 | ( | 3.4% | ) | | 1000 | : | 1 | ( | 3.4% | ) |
|
 |
2138
(98.7%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
LA County
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
psa_lab_value
[numeric] |
All NA's
|
|
|
189
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Northern CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
psa_lab_value
[numeric] |
All NA's
|
|
|
174
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Greater CA
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
psa_lab_value
[numeric] |
All NA's
|
|
|
237
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Detroit
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
psa_lab_value
[numeric] |
Mean (sd) : 49 (186.5)
min < med < max:
1 < 15 < 1000
IQR (CV) : 12.5 (3.8) |
| 1 | : | 1 | ( | 3.6% | ) | | 2 | : | 1 | ( | 3.6% | ) | | 3 | : | 1 | ( | 3.6% | ) | | 4 | : | 1 | ( | 3.6% | ) | | 5 | : | 1 | ( | 3.6% | ) | | 6 | : | 1 | ( | 3.6% | ) | | 7 | : | 1 | ( | 3.6% | ) | | 8 | : | 1 | ( | 3.6% | ) | | 9 | : | 1 | ( | 3.6% | ) | | 11 | : | 1 | ( | 3.6% | ) | | 12 | : | 1 | ( | 3.6% | ) | | 13 | : | 1 | ( | 3.6% | ) | | 14 | : | 1 | ( | 3.6% | ) | | 15 | : | 2 | ( | 7.1% | ) | | 16 | : | 1 | ( | 3.6% | ) | | 17 | : | 2 | ( | 7.1% | ) | | 18 | : | 1 | ( | 3.6% | ) | | 19 | : | 1 | ( | 3.6% | ) | | 20 | : | 1 | ( | 3.6% | ) | | 21 | : | 1 | ( | 3.6% | ) | | 22 | : | 1 | ( | 3.6% | ) | | 23 | : | 1 | ( | 3.6% | ) | | 24 | : | 1 | ( | 3.6% | ) | | 25 | : | 1 | ( | 3.6% | ) | | 26 | : | 1 | ( | 3.6% | ) | | 1000 | : | 1 | ( | 3.6% | ) |
|
 |
146
(83.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Louisiana
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
psa_lab_value
[numeric] |
All NA's
|
|
|
290
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Georgia
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
psa_lab_value
[numeric] |
1 distinct value |
|
 |
1086
(99.9%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01
Michigan
| No |
Variable |
Stats / Values |
Freqs (% of Valid) |
Graph |
Missing |
| 1 |
psa_lab_value
[numeric] |
All NA's
|
|
|
16
(100.0%) |
Generated by summarytools 0.9.8 (R version 4.0.3)
2021-04-01